Disaggregation of Residential Home Energy via Non-Intrusive Load Monitoring for Energy Savings and Targeted Demand Response
by Jeremy Hare
B.S., Aerospace Engineering, University of Michigan - Ann Arbor, 2006 M.S., Aerospace Engineering, University of Michigan - Ann Arbor, 2007
Submitted to the Department of Civil and Environmental Engineering and the MIT Sloan School of Management in partial fulfillment of the requirements for the degrees of
Master of Business Administration and Master of Science in Civil and Environmental Engineering
in conjunction with the Leaders for Global Operations Program at the Massachusetts Institute of Technology
June 2018
C Jeremy Hare. All rights reserved.
The author hereby grants to MIT pennission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Signature redacted Signature of Author: S r Department of Civil and Envircfimei al Engineering and MIT Sloan School of Management May 11, 2018 redacted Certified By: _Signature Georgia Perakis, Thesis Advisor liam F. Pounds Professor of Management Science Z70 Certified By: Signature red acted John R. Williams, Thesis Advisor /, ro~e/sor of Civil and Environmental Engineering
Certified By: Signature redacted Kostya Turitsyn, Thesis Advisor iate Professor oSMechanical Engineering Approved By: Signature redacted Herson Director, MBA oqram/4JT Sloan School of Management
Approved By: ___Signature redacted______rol Jesse Kroll Chair, Graduate Program Comn/ittee, Civil and Environmental Engineering MASSACHUSETTS INSTITUTE OF TECHNOLOGY co W 18 7 20 L JUN LIt3RARIE.S Disaggregation of Residential Home Energy via Non-Intrusive Load Monitoring for Energy Savings and Targeted Demand Response by
Jeremy Hare Submitted to the Department of Civil and Environmental Engineering and the MIT Sloan School of Management on May 11, 2014, in partial fulfillment of the requirements for the degrees of
Master of Science in Civil and Environmental Engineering and Master of Business Administration Abstract
Residential energy disaggregation is a process by which the power usage of a home is broken down into the consumption of individual appliances. There are a number of different methods to perform energy disaggregation, from simulation models to installing "smart-plugs" at every outlet where an appliance is connected to the wall. Non-Intrusive Load Monitoring (NILM) is one such disaggregation option. NILM is widely recognized as one of the most cost-effective methods for gathering disaggregated energy data while maintaining a high level of accuracy. Although the technology has existed for many years, the adoption rate of NILM, and other devices that disaggregate energy, has been minimal. This thesis provides details on the potential benefits, both for the customer and utility provider, associated with furthering the adoption of NILM devices and obtaining the disaggregated appliance level energy-use. A broad overview of potential benefits is presented; however, the primary goal of this thesis will be to investigate two benefits of NILM in detail: overall household energy reduction and targeted demand response. First, installation of a NILM device can provide electricity customers information that allows them to become more aware of their energy consumption, and thereby, more energy efficient. A study was conducted that looked at the electricity consumption of 174 homes that were using a passive NILM device in their home. This NILM device provided immediate feedback on the power consumption for a portion of the home's appliances via smart-phone application. The homes reduced their monthly energy consumption by an average of 2.6 - 3.1% after the NILM installation. This was validated by a number of analysis methods returning
I similar results. Aligned with this benefit comes a recommendation for an incentive structure that can reduce the price paid by the consumer and develop a higher adoption rate of NILM devices. Second, the wide-spread adoption of NILM devices can provide electric utilities information to reduce carbon intensity via targeted demand response. There is a significant opportunity for utilities to engage their customers based on the time of use of detailed appliances. Multiple metrics are presented in this thesis to quantify the deferrable load opportunity of specific appliances and individual households. Utility operational cost savings and greater customer incentives can be linked to the use of these metrics.
Thesis Supervisor: Georgia Perakis Title: Professor, Operations Research and Operations Management
Thesis Supervisor: John Williams Title: Professor, Civil and Environmental Engineering
Thesis Supervisor: Kostya Turitsyn Title: Associate Professor, Department of Mechanical Engineering
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32 Acknowledgements
To my thesis advisors Georgia Perakis, Kostya Turitsyn, John Williams, thank you for your willingness to work through issues and challenge my ideas.
To my Company X supervisors, thank you for the flexibility and support of this research. Additional thanks to my colleagues and advisors at Company X, thank you all for your time and energy - it was great working with each one of you over the six-month research fellowship.
To the LGO program - this was one of the most challenging, yet rewarding experiences of my life. Thanks to everyone who have made the classes, internships, partner company relationships, and the MIT community work so well to complement one another.
To my LGO classmates - It was a privilege to have gotten to know each one of you over the last two years! It is a great honor to be a part of the LGO Class of 2018!
And I want to thank my better half - my wife Maria. You make me a better person every day with your encouragement, patience, and love.
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5 Contents Chapter 1 - Introduction ...... 9 1.1 Problem Statem ent ...... 9 1.2 NILM Overview ...... 10 1.3 M ethods of Disaggregation ...... 11 1.3.1 Surveys ...... 11 1.3.2 Direct M etering of Appliances ...... 12 1.3.3 Statistical M odeling via AMI ...... 13 1.3.4 Hardware Sensors ...... 14 1.4 NILM Literature review ...... 15 1.4.1 NILM for Energy Efficiency ...... 17 1.4.2 NILM for Targeted Dem and Response ...... 18 1.5 Thesis Contributions...... 19 Chapter 2 - Residential Energy Consum ption Reduction ...... 20 2.1 Introduction ...... 20 2.2 Data for Study ...... 20 2.3 Experim ental Setup ...... 22 2.4 Analysis M ethods and Results...... 23 2.4.1 M odel 1 - Basic Average Com parison ...... 23 2.4.2 M odel 2 - M onthly Norm alization ...... 25 2.4.3 M odel 3 - Seasonality Rem oved & M onthly Norm alization ...... 26 2.4.4 M odel 4 - Seasonality Removed with Linear Regression...... 27 2.4.5 M odel 5 - Seasonality Rem oved with Linear Regression+ ...... 29 2.4.6 M odel 6 - Consistent Tim e Frame Com parison ...... 31 2.5 Sum m ary of Results...... 32 2.6 Lim itations and Recom mendations...... 33 2.7 Policy Recom m endation ...... 35 Chapter 3 - Targeted Dem and Response ...... 38 3.1 Introduction ...... 38 3.2 Data for Study ...... 40 3.3 Quantified Flexibility Potential ...... 41 3.3.1 General Appliance Deferral Potential ...... 41 3.3.2 Peak Dem and Flexibility ...... 43 3.3.3 Use of Peak M etric Term s ...... 45 3.4 Lim itations and Recom m endations...... 50 Chapter 4 - Additional NILM Use-Cases ...... 52 4.1 NILM Benefits ...... 52 4.2 Risks and M itigation ...... 54 Chapter 5 - Conclusions...... 55 5.1 Recom m endations ...... 55 5 .1 .1 U tilitie s ...... 5 5 5.1.2 NILM M anufacturers ...... 55 5 .2 S u m m ary ...... 5 7 A p p e n dix ...... 5 8 B ib lio g ra p h y ...... 5 9
6 Figures Figure 1: Disaggregation via Non-Intrusive Load Monitoring ...... 15 Figure 2: Number of NILM publications per year ...... 16 Figure 3: Locations of Homes in Energy Efficiency Study ...... 21 Figure 4: Model 1 Results...... 23 Figure 5: Distribution of HEM Installation Month...... 24 Figure 6: M odel 2 R esults...... 26 F igure 7: M odel 3 R esults...... 27 F igure 8: M odel 4 R esults...... 28 Figure 9: Model 4 Focused Subset Results...... 28 F igure 10: M odel 5 R esults...... 3 0 Figure 11: Model 5 Focused Subset Results...... 30 Figure 12: Model 6 Results...... 32 Figure 13: Price of Electricity (July 18, 2017) ...... 39 Figure 14: Locations of Homes in Disaggregation Study...... 41 Figure 15: Refrigerator's Power Cycle...... 42 Figure 16: Summary of Top Deferrable Appliances...... 43 Figure 17: Summary of Peak Usage Deferrable Appliances ...... 44 Figure 18: ISO-NE Electricity Demand (July 19, 2017) ...... 46 Figure 19: Potential for Deferral Based on Metric ...... 46 Figure 20: New England Demand Reduction Sensitivity Analysis ...... 49
7 Tables
Table 1: Pecan Street Results of EEme Disaggregation ...... 14 Table 2: Analysis Results Summary ...... 32 Table 3: Deferrable Potential of Appliances...... 43 Table 4: Peak Deferrable Potential of Appliances...... 45 Table 5: Summary of Peak Load Statistics...... 49
8 Chapter 1 - Introduction
1.1 Problem Statement Most individuals have little knowledge of how energy is distributed throughout their home. In a 2010 survey of over 500 US residential electricity customers, energy use of appliances was underestimated by a factor of 2.8 on average (1). In other words, individuals have a flawed perception on how much energy is being consumed (and wasted) within their home. Non- Intrusive Load Monitoring (NILM) is one of the highest potential methods to alter this knowledge gap. NILM is a technology that analyzes the steady state and variable power from a single source. This analysis leads to a disaggregation of the power to determine how it is distributed to different appliances throughout the building. In a primary residence context, it could provide information to a resident that they previously did not have access to. The thought is that a residence that has this information would take action to reduce their energy usage. However, introducing NILM into residential homes has a greater importance than just increasing knowledge and reducing the costs of their electricity bill. In 2015, the electric power industry was the largest single sector contributor to greenhouse gas emissions in the United States (2). Residential electricity customers were the largest portion of this energy usage, and in turn, made up the highest contribution to emissions; more than commercial or industrial customers (3). While being the highest aggregate source of emissions, residential electricity consumers are generally unaware of their household energy use until their monthly energy bill arrives. NILM can act as that bridge of engagement between the household electricity use and the user, and motivate to reduce emissions. With a NILM installed in a residential home, an individual knows the exact costs associated with running the air conditioner or leaving the lights on downstairs. This information by itself can deter individuals from energy wasting habits. Paired with specific savings suggestions, comparison and competition with neighbors, and incentives for improving energy efficiency, there is a significant potential for this technology to shift behaviors. Even small behavioral changes at the individual residence level that are widespread can add up to a large impact on society's largest emissions source. In general, NILM is a technology that has a large potential benefit associated with its widespread usage. These benefits span between multiple stakeholders, including individual
9 customers, the electric utility provider, and society at large. At the time of this paper being published, there is no clear adoption strategy associated with the use of this technology. This paper further develops two of the key benefits associated with NILM and provides incentive recommendations associated with each benefit. The first benefit is studying the effect of a NILM installation in 174 New England homes. Their electricity usage before and after the NILM installation was compared. General energy reduction is important for saving on limited resources. A number of government sponsored incentives are being offered for energy saving appliances installed throughout the home. If NILM devices can prove to reduce energy across a wide sample of customers, a similar incentive program should be considered for these devices. The second benefit is how NILM can be used as a tool for peak demand reduction. The electric grid capacity is determined by the forecasted maximum electricity demand, better known as peak demand. A sustained reduction in the peak demand can be tied to a reduced cost for utility infrastructure, a reduction in energy purchases from the most expensive generation plants, and savings on the deferral of transmission and distribution (T&D). Aspects of flexibility and power consumed are discussed, with a target appliance range of flexibility and power. An evaluation metric is presented to quantify an appliance deferral potential based on these two aspects of deferral potential.
1.2 NILM Overview Energy disaggregation is a process by which the overall power usage of a home is broken down into the power consumption of individual appliances. There are a number of different methods to perform energy disaggregation, from simulation models to installing "smart-plugs" at every outlet where an appliance is connected to power. Non-Intrusive Load Monitoring (NILM) is one such disaggregation method. NILM is widely recognized as one of the most cost-effective methods for gathering disaggregated energy data while maintaining a high level of accuracy. NILM is a method to disaggregate power usage from a single household power-use measurement down to specific appliance usage. NILM makes predictions about which appliances are running by recognizing different patterns and appliance "load signatures". Included within an appliance load signature are the specific power consumption, run time, time-of-use, phase, and harmonics of the device, and other identifiers.
10 NILM devices can identify these key attributes, and filtering these signals from the noise and other devices running at the same time.
1.3 Methods of Disaggregation Disaggregation via a NILM hardware device will be the primary focus of this paper. However, this is just one of the primary methods for disaggregation of household electricity demand. We will review four different methods of disaggregation of a household power demand: surveys, direct metering, statistical modeling via Advanced Metering Infrastructure (AMI), and hardware installed NILM.
1.3.1 Surveys One of the lowest cost methods of individual household energy disaggregation is conducting surveys. Surveys are typically conducted on a representative sample of a larger population and results are interpolated to make an estimate about the out-of-sample population. For residential home energy surveys, a survey will make an estimate of the total power consumption based on the appliances that have been listed as part of the home. Surveys of electricity customers on their consumption can be flawed due to multiple reasons. First, the survey results are only as good as the participants' input. When asking a customer how many refrigerators are running in their house, they may forget about that old fridge in the basement that is rarely opened, but is still consuming electricity. In addition to reporting unintentional inaccuracies, questionnaires themselves can introduce biases that will increase error in the survey reports. Short, clear, and understandable questions will have the most accurate responses, but often the amount of information the surveyor will want to abstract is at odds with this concise recommendation. As one of the largest surveys of energy disaggregation, the Energy Information Association's (EIA) Residential Energy Consumption Survey (RECS) provides a depiction of the electricity use of American residences. The survey was first conducted in 1978, initially under the name of the National Interim Energy Consumption Survey (NIECS). The original NIECS has similarities to what you will find published today: approximations for amounts of homes using air conditioning, types of heating fuels used, and conservation efforts performed throughout the year (with many other items covered). The survey was conducted every year until 1984, was
11 scaled back to a three-year cycle until 1996, on a four-year cycle until 2009, and is currently conducted every six-years. In 2015, the survey used a combination of in-person interviews and questionnaires to gather data on 5,686 homes from across the country. The data is then put through statistical modeling to determine how the results of the surveyed households apply to other households outside the sample population. The selection of the households involved in the study is highly scrutinized before selection to ensure it is a representative sample of the 118.2 million primary households in the United States (4). The RECS provides general insights of household energy consumption, long-term trends over time, and are used to shape national energy policy by the EIA and United States Department of Energy. The RECS does not provide any detail information past the state level. In fact, 2009 was the only state-level characterization of data, as 2015 did not receive enough respondents for reporting state-level information. Depending on the objective, this high-level picture of energy consumption can be adequate for modeling purposes. However, when focused on the targeting of specific homes, states, or regions, applying RECS results would provide a large error. We will use the RECS survey results when discussing general trends in the electric marketplace in Chapter III in order to estimate how many homes have an electric hot water heater and air conditioner in New England.
1.3.2 Direct Metering of Appliances Another method of disaggregation is to use "smart-plug" sensors, inserted between an appliance and their respective wall outlet. This disaggregation method is often referred to as direct metering or intrusive load monitoring. This method provides a very accurate power measurement of each appliance; however, it comes at a high cost of purchase and can be timely and disruptive to install throughout the home. A study performed by the Pecan Street to direct meter 700 volunteer homes in Austin, TX was funded by a $10.4M Department of Energy (DOE) grant. The Pecan Street Dataport is one of the largest publically available datasets for disaggregated home energy usage. The cost of directly metering the homes was $60-200 per outlet, or approximately $2000 per home (5), although plug hardware costs have come down since the study was conducted. Current hardware costs for smart plugs vary depending on the functionality offered and user interface. Etekcity@ is
12 currently offering their Wi-Fi-enabled, power monitoring smart plugs for $12.50 each (when purchasing four (6)) while WeMo energy monitor smart-plugs are offered at $40 per plug (7). Direct metering of appliances does have his benefits. It accurately depicts the power usage of an appliance, can give the user wireless control of the appliance and can often be put on an automated schedule. However, at the high cost of installation for direct metering, it is unlikely the wide-spread adoption of this method of disaggregation.
1.3.3 Statistical Modeling via AMI Another disaggregation method makes predictions of individual home appliance consumption based on reading from advanced meter infrastructure (AMI). AMI captures the total power usage of a home at specific time intervals. This is typically at a 15-minute interval for the most common type of meter in the market today. This interval power consumption can be paired with certain characteristics of the home (square footage, occupancy, number of stories, efficiency, etc.) along with external characteristics (weather, neighborhood comparison, etc.) in order predict what appliances are running at any given time based on a total power consumption. One significant benefit of this method of disaggregation is the wide-spread adoption rate of the AMI. In 2016, there were over 62M residential AMI installed around the United States, which accounts for 47% of all residences (8). This effort has been sponsored by utilities across the country to enhance communication with customers and advance the "smart-grid" eventually to a support more awareness of time-of-use. It has been shown that disaggregation from using AMI power usage data can have significant variability for predicting appliance usage accurately. Pecan Street published two papers (9) (10) for a disaggregation service, EEme. Appliance level usage was predicted based on the total household interval power consumption. The AMI predictions were then compared to Pecan Street's "ground-truth" dataset based on their direct metering. In the first study, reported by Pecan Street in 2015, EEme was provided 15-min interval household energy usage data (total power usage) for 264 homes over a twelve-month period, along with the corresponding weather data. EEme ran this data through their disaggregation algorithms and predicted which appliances were running at what time. This study was attempting to predict the overall monthly consumption of appliances within the home. The second study was similar to the first, except the interval of the power usage data was reduced to 1-second, the
13 number of homes was reduced to 10, and the total historic time used was 77-weeks. This study was attempting to predict the hourly usage of a set of appliances. Results for each study are shown in Table 1. It is hard to compare the two results as the 15-minute disaggregation study reports an average monthly error rate, and the 1-second disaggregation study reports an average hourly error rate. It's possible the 1-second average hourly error rate could aggregate to a larger average monthly error rate, but it is unclear from the results how the two compare. Also, the 1-second data was only based on a limited sample of 10- homes. What is clear from the study results is that the AMI disaggregation approach used in these studies would not be ideal for real-time disaggregated feedback and energy efficiency validation of appliances. It may be acceptable for some applications i.e. providing feedback to customers on their monthly appliance usage, but it is heavily dependent on the magnitude of error that is acceptable in each application considered.
Error RatesA 1-Second* 15-Minute** HVAC -2% -31% Refrigerator -9% -28% Clothes Dryer 1% -45% Dishwasher 11% 33% Electric Vehicle -5% NA * Average hourly error rate, **Average monthly error rate, ANegative error rates are underestimates, positive error rates are overestimates
Table 1: Pecan Street Results of EEme Disaggregation
1.3.4 Hardware Sensors George W Hart introduced hardware sensor Non-Intrusive Load Monitoring (NILM) in his 1992 paper (11). Since then, NILM has improved in its methods of disaggregation. There are a number of companies that are offering NILM hardware, and although the hardware monitors have gained popularity in the residential market among energy enthusiasts, they are not as nearly wide-spread as AMI. This is likely because of the lack of utility investment in NILM, where AMI received a number of large grants to upgrade the metering system in recent years.
14 The NILM hardware sensor is installed at a single point within the residence, typically within the circuit breaker panel. The monitor can gather specific data from the break panel connection including real power, reactive power, harmonics, voltage, noise, among other features (12). This aggregate information is matched to a database of known appliance "load-signatures" and from there, a prediction is made about what appliances are running at any given time in the home. As shown in Figure 1, a NILM sensor will break down different appliance usage over time depending on their load signature.
6
4
40 60 Time (min) Figure 1: Disaggregationvia Non-Intrusive Load Monitoring (13)
The primary focus of this paper is around how NILM hardware sensors can benefit the understanding of energy consumption for both the residential customer and the utility.
1.4 NILM Literature review Although the technology for NILM has been around for over 25 years, there has been a significant increase in interest in the technology in recent years. This is evident by the number of studies that have been published about NILM and the sudden increase in recent years. Reference Figure 2 for a graphic example of this finding.
15 160
140
120
100
80 0
E 60
40 .0
20
0 1995 2000 2005 2010 Year Figure 2: Number ofNILMpublicationsper year (14)
For a comprehensive survey of different methods of Non-Intrusive Load Monitoring, reference Zoha, et. al., 2012 (15). Within their paper, the authors provide details of the techniques associated with different NILM systems for appliance recognition via energy disaggregation. A general framework of how NILM categorizes appliances based on their load signatures, how these features are extracted and analyzed, and how the state-of-the-art of NILM has moved from supervised learning for pattern recognition to unsupervised learning over the last few years. The paper also provides a clear recommendation for researchers to adopt a common NILM accuracy metric in order to make an adequate comparison between methods. Currently, no such metric exists across studies.
The National Renewable Energy Laboratory (NREL) published a paper and presentation in 2012 describing the potential applications of NILM to different stakeholders including customers, technology sector, service sector, and utilities (16). Near-term applications of NILM described in the paper include customer and utility gains in efficiency based on appliance level information. Longer-term applications include more automated appliance control of the home based on occupancy as well as predicting faults and failures of appliances. The remaining portion of literature review will focus on specific use-cases associated with NILM including energy efficiency and targeted demand response.
16 1.4.1 NILM for Energy Efficiency
Jack Kelly, 2017 (17), provided a thorough review of twelve energy efficiency studies associated
with residential disaggregate electricity usage. Each study was categorized based on having
controlled for volunteer bias, the Hawthorne effect, and weather. In addition, just having a
control group for comparison was categorized in each study. He claims "to the best of our
knowledge, (his thesis) is the first systematic review of domestic, disaggregated electricity feedback." Within this paper, Kelly found a wide range of feedback mechanisms to provide the user
information on their home appliances. The average reduction within the twelve studies that were
reviewed was 4.5%, which was weighted by the number of households participating in each
study. Between the studies, the standard deviation of reduction 10.1%. The greatest effect was found in an EnergyLife study performed by Gamberini in 2012 which found that disaggregate energy feedback reduced home energy consumption by 38%. The smallest effect was a study by McCalley and Midden in 2002 at 0%. Both the minimum and the maximum usage reduction studies were not for the entire household, but rather a portion of the home (a specific appliance or multiple appliances that were monitored). This is one of the more comprehensive reviews of disaggregate data and its potential for energy savings within the "energy enthusiast" community. Gupta and Chakravarty, 2014 (18), of Bidgely, studied power usage in California for residential electricity customers who were provided Bidgely's disaggregated services. The results showed for a single month there was a 6% reduction for customers exposed to Bidgely's disaggregated services compared to customers who were not exposed to the disaggregate information. Although, there is a positive bias associated with customers who opted-in to the program and chose to have Bidgely services. Another study performed with Bidgely with Pacific Gas and Electric (PG&E) in California resulted in a 7.7% reduction of customers who were signed up for PG&E's time-of- use (TOU) rates (19). Electricity customers who voluntarily sign up for TOU pricing are assumed to be a positive biased sample of customers. Although these are likely the individuals who have already made energy efficiency progress within their home prior to the study.
Regardless of which bias had more impact on the result, this energy reduction result cannot be directly expected in the general population.
17 1.4.2 NILM for Targeted Demand Response
A study completed by Navigant Consulting in partnership with the Advanced Energy Economy
(AEE) found that 10% of the country's electricity system is built to meet 1% of the year's hours. This gets to the heart of the need for electricity demand flexibility and the reduction in peak load. If that top 1% of peak load hours is reduced, there are infrastructure cost savings for the utility
which translates directly to the rate payer. The same study found for every $1 spent on DR, benefits were in the range of $3.26 - $4.07 in Massachusetts. These benefits included capacity
market cost avoidance, high energy cost avoidance, transmission and distribution cost avoidance,
and greenhouse gas emissions cost avoidance (20). In a general feedback study, Bidgely worked with Australia's United Energy (UE) to trial
a demand response program (21). The study tracked customer usage and provided real-time
feedback during the peak-load events that were called four times over the course of Summer
2015-2016. Gamification via goal setting, immediate real-time feedback, and real-time award confirmation for incentives were part of the Bidgely's "ActionDR" product that was provided to UE's customers. Bidgely reported an "average peak load shift of greater than 30% per user per
event." It is unclear how many homes were involved in this trial-run, however, a greater than
30% reduction in peak usage with no direct appliance recommendations is a noteworthy
accomplishment and should be taken into consideration for future work. The categorization of appliances between deferrable and non-deferrable appliances is
reviewed in this 2017 publication, "Non-intrusive load monitoring through home energy management systems: A comprehensive review" (22). The article details deferrable / thermostatic appliances (DTA), which are "appliances capable of providing grid services without jeopardizing the quality and reliability of their primary function according to users' comfort level, and satisfaction" - in other words, appliances that can shift their time of use without the user even realizing. Examples of these appliances within the residence are the electric hot water heater and air conditioner. These specific deferrable appliances will be the subject of the targeted demand response metric reviewed in Chapter III.
18 1.5 Thesis Contributions The academic achievements of this thesis are as follows:
1. Quantifies the reduction in residential energy usage after a NILM device installation 2. Provide guidelines to improve future energy efficiency studies 3. Identifies a NILM incentive based on similar marketplace efficiency 4. Provides a construct for using appliance-level time-of-use data for demand response evaluations
First, home energy usage before and after installing a NILM device has been studied. Prior to the NILM installation, it was assumed customers only had access to a total electricity consumption via the monthly bill provided from their utility provider. The NILM device provided both overall power consumption and appliance-level consumption information in real- time via a smart-phone application. The hypothesis is that this additional information was used to take action to reduce overall energy usage. We have tested this hypothesis against actual customer energy usage data on 174 homes and have confirmed a 2.6 - 3.1% reduction after the a NILM device was installed. We also show how altering the analysis method effects the results. As a second primary contribution, this thesis provides a list of recommendations for performing analysis of energy efficiency studies. There are a number of improvements that could be made within data collection, and study design that are reviewed. A third contribution of this thesis provides a recommended incentive strategy associated with NILM promoting energy efficiency within the home. A rebate could be provided by federal or state government funds based on the efficiency improvements gained. This type of rebate for NILM devices does not currently exist. A fourth contribution of this thesis answers the question of how to evaluate the appliance time of use for different applications. This thesis provides a simple deferral metric that can be calculated for each appliance to determine the available shift in demand that could occur. With this metric, each appliance within the home can have its own score of deferral potential. These individual appliances can be aggregated to determine a household deferral score. This household deferral score can be paired with recommendations from the utility for involvement in demand response programs or time-of-use rates. The individual terms of the metric, flexibility and power consumption, can be used for evaluating the shift of appliance usage outside of a peak load timeframe.
19 Chapter 2 - Residential Energy Consumption Reduction
2.1 Introduction The majority of residential electricity customers have a knowledge gap between using their appliances and paying their electricity bill. The bill generally comes on a monthly cycle, and automatic payments online make it easier to ignore the energy bill all together, resulting in less concern for electricity consumed within the home. Unless a bill is significantly higher than expected, the home's residential electricity consumption is an afterthought for most customers. NILM attempts to change this typical monthly energy feedback in a significant way. With a NILM device installed in the home, immediate feedback associated with electricity usage is possible. The goal behind this study was to determine if there was a reduction associated with that feedback, i.e. are residential customers taking action on the feedback they are receiving? Using a number of different analysis methods, the monthly energy usage of 174 homes with a NILM device installed was reviewed. Looking at all the different analysis methods, the average household energy reduction was 2.3%. This reduction varied between 1.0 - 3.1% depending on the analysis method, if outliers of the data were removed, type of weather normalization, and seasonality normalization. Only considering the effects within +/- 2y from the mean results in a 2.6 - 3.1% reduction on average. Each of these analysis methods are explained in more detail in the Analysis Methods and Results Section 2.4.
2.2 Data for Study Company X is a distributor of electricity to a large portion of the New England population. The data used for this research was based on 174 Company X electricity customers that had ordered and installed a NILM Home Energy Monitor (HEM). The installation date of the HEM was matched with the home's monthly power consumption data. An automated matching algorithm was used to link the shipping address of the HEM to a billing account, which could output a random house number to the installation date of the HEM, the monthly power bill (in kWh), and the zip code of the home. This automated matching algorithm was used to keep all detailed customer information anonymous. Figure 3 shows the distribution of home locations that were a part of the analysis.
20 40
Key (Monitorm per zip code)-'' I Monitor *
5 9 Monitors
Figure 3: Locations of Homes in Energy Efficiency Study
To be included in the study, the residence needed at least two months of usage data after the HEM device was installed and 12-months of usage information before the HEM was installed. Any "net-metering" homes within the data, which is linked to solar-generation, were removed as the information available was only for total billed kWh, rather than the actual energy used within the homes. In order for account for seasonality pattern recognition within the household usage data, there needed to be a minimum of 24-continuous months of bill usage at the household billing account available. This was a requirement of the software package used to detect seasonality effects (R's seasonal trend decomposition). In other words, the billing account information needed to be continuous for two years. For the analyses that required seasonality decomposition, this eliminated a number of homes from the original 174, down to 146 residences.
21 The zip code of the house was incorporated to control for weather in one model. The number of heating degree days (HDD) and cooling degree days (CDD) for each weather station location in the state was averaged to produce a state-wide HDD and CDD. A heating degree day (HDD) is a form of degree day used to estimate energy requirements for heating while a cooling degree day (CDD) is for cooling (air conditioning and refrigeration). The daily HDDs and CDDs were calculated by the different between the specific day's average temperature and 65'F. For example, a day with an average temperature of 75'F is categorized as 10 CDD, 0 HDD. A day that has an average temperature of 55'F would be 0 CDD, 10 HDD. Every weather station in New York, Massachusetts, and Rhode Island was incorporated into a state-wide average number of HDDs and CDDs for each month of study. This method of "average state-weather" was used in a Model 5's analysis to incorporate the effect of weather in combination with the effects of seasonality and installation of the HEM.
2.3 Experimental Setup In each of the studies detailed in the energy efficiency literature review of Section 1.4.1, recommendations of how to reduce consumption were presented to residential electricity customers. This experiment was performed with a HEM that did not provide specific recommendations on how customers can reduce their energy usage. Instead, the HEM provided an interface for customers to view their household energy usage in real time, broken down into individual appliance consumption. The goal of the experiment was to understand the effect of installing this HEM on a home's power consumption. Individual homes power usage before and after HEM installation was compared. The "control group" was the individual households prior to HEM being installed. The "treatment group" was the individual households after HEM was installed and determining the effect. The Hawthorne Effect is defined as the "alteration of behavior by the subjects of a study due to their awareness of being observed (23)." The Hawthorne Effect was controlled within this research by matching historic usage information for those customers who sought out the purchase of the specific HEM. The customers continued their normal activities without knowledge of being involved in this study.
22 One primary assumption used in calculating the results was that extreme changes of power consumption were not directly caused by the HEM. It was assumed that any large shift in a residential home's energy usage (positive or negative) was due to outside factors that were not taken into account in the model. The analysis results below show all homes in the dataset as well as comparing it to just the homes within +/- 2 standard deviations (+/- 2a) from the mean. This calculated the effects of the HEM with the extreme values of change removed.
2.4 Analysis Methods and Results
There were six different analysis methods used to examine the individual household usage data, each of the methods returned a different result ranging from 2.6% to 3.1% reduction after removing the extremes (+/- 2a remained). Analyzing the limited dataset with multiple techniques confirmed that the relative reduction in usage was not due to the type of analysis used. The analysis method without any normalization was excluded from these results for reasons that will be discussed further in detail in the Model 1 section below.
2.4.1 Model 1- Basic Average Comparison This simple analysis method was used as a reference point, however was not included in the results. It compared the kWh billed before and after the HEM installation for each of the 174 residential homes in the dataset. The results showed that 91 of the homes increased their usage and 83 reduced their usage after the HEM installation. The average effect showed a 1.7% increase in kWh billed after HEM installation. Reference Figure 4 below for the distribution of results for all homes and the homes within +/- 2a from the mean.
AN Homes (174 total) +/- 2a from Mean (163 Homes) Decreased Usage Increased Usage Decreased Usage increased Usage 83 Homes 91 Homes 79 Homes 84 Homes
2 Avg - 1.7% Increase Avg -0.4% Inarease
IL
I f I I I
-0.5 0.0 0.5 1.0 -0.6 -0.4 -0.2 0.0 0.2 0.4 Relative change in electricity consumption before and after HEM installation (0.0 = No Change) Figure 4: Model 1 - Basic Analysis Results (No seasonality or normalization)
23 This was not included in the summary results stated in the paper's abstract, but it is worth mentioning why it was excluded. Since this model does not take into account any seasonality patterns, it is heavily dependent on when the HEM was installed in the home. Figure 5 below shows a distribution of when the HEM was installed in each of the homes included in the study. 49% of the HEM devices were installed in the months of November, December, January, or February. When compared to the summer months of May, June, July, and August, which only represented 24% of the total installation months, the winter months have a lower overall power consumption. This the overall trend in the United States, and was seen within this particular dataset. As an example, in the United States, winter months of 2016 (January, February, November, and December) accounted for 15% less energy consumption from the residential sector than did the summer months (May, June, July, August) (24). With the HEM expected to account for a single digit reduction in energy usage for a home, the seasonal usage would dominate the effect of the HEM. The average time the HEM was installed in the dataset was 7.5 months. Including the summer months and not the winter months in that portion after installation would skew the results. A HEM installed in the winter would include the summer months in the "after HEM" effect. As the analysis only compared the household consumption before and after HEM installation, with the limited number of months included in the after-HEM installation data would skew the results to have an "increased-use" bias.
Distribution of Month Installed
30
25
' 20
,.15
10
5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 5: Distributionof IEM InstallationMonth
24 Based on this information, the month of installation would be a significant factor that could drive the relative change in monthly energy use. For this reason, the results of the analysis Model 1 were not included in the overall results, but for transparency of results and avoidance of errors of any studies pursuing energy efficiency measures in the future, they are worth discussing. Comparing direct usage data with imbedded seasonality effects, as Model 1 does, would be a reasonable option to consider with an alternate analysis approach. A control group of "similar" houses that did not have a HEM installed could be used to compare. The similar homes could be based on a monthly power billed (within a certain range) before the HEM installation date. The effect of the HEM installation would be the primary change between the houses, which could be measured for statistical significance. Ideally additional data would be available to compare these "similar-use" homes to one another. This was left for future work as discussed in the Section 2.6 Limitations and Recommendations.
2.4.2 Model 2 - Monthly Normalization To identify and remove consistent monthly trends in the usage data, a monthly ratio was used to normalize the power data from the 174 homes. This analysis method normalized each month's consumption to the average monthly consumption for that specific household. As an example, a "January Factor" was used for normalizing all consumption data from January. This factor took the overall average consumption for the house and divided it by the average January consumption. Each month had its own normalizing factor that was multiplied to the actual consumption. When the average consumption for January was lower than the average overall consumption, the month's relative consumption was increased. When determining the effect that the HEM installation had on the monthly consumption, the normalized average consumption before the installation was compared to the normalized average consumption after installation. This resulted in 99 homes with a reduced usage and 75 homes with an increased usage. The average effect was a 1.0% decrease in the monthly usage after the HEM was installed. Removing the extremes on both ends of the results, and only looking at +/- 2a from the mean resulted in an average decrease in consumption of 2.6%. Figure 6 below shows a distribution of the results of Model 2.
25 All Data (174 Homes) +/- 2cr from Mean (162 Homes) Decreased Usage increased Usage Decreased Usage Increased Usage 99 Homes 75 Homes 95 Homes 67 Homes
Avg - 1.0% Decrease Avg - 2.6% De crease C U,
0) ......
zizz z~jIifl rn 2 I-I---. K~ .0 _5 0.0 00 10 04 4Z 00 02 04 Relative Change in Electricity Consumption Before and After HEM Installation (0.0 = No Change) Figure 6: Model 2 Results - Monthly NormalizationAnalysis
2.4.3 Model 3 - Seasonality Removed & Monthly Normalization This analysis technique used a method to normalize the monthly consumption of each household after the individual household's seasonality trends were removed. Using's R's seasonality decomposition base-algorithm "decompose", the time series data was identified into three additive portions: a seasonal component, a trend component, and a random component.
Yt = Tt + St + Et Y, is the usage time series, T is the trend, St is the seasonalitypattern, and c, is the random remainder
The seasonality component of the usage data was removed, leaving the trend and random components. With the seasonality component removed, the average use was compared before and after the HEM installation. As the decomposition algorithm requires a minimum of two years of data, 28 of the homes in the dataset did not meet this requirement and were removed from the analysis. The remaining 146 homes were analyzed and included in this analysis model. The results of Model 3 "Seasonality Removed" analysis showed 83 homes reducing their consumption and 63 increasing their consumption. The average effect was a 2.7% decrease, with a standard deviation of 26%. After removing the extremes outside of +/- 2a the average consumption decrease before and after HEM installation was 2.6%. Reference Figure 7 below for the distribution of results.
26 All Data (146 Homes) +/-2a from Mean (136 Homes) Decr eased Usage Increased Usage Decreased Usage Increased Usage 83Homes - 63 Homes R - 78 Homes 58 Homes
Avg - 2.7% Decrease Avg - 2.6% D ecrease U a)C 0' a) LL
-0.5 MO 0. 1.0 -A -0.2 00 02 0.4 Relative Change in Electricity Consumption Before and After HEM Installation (0.0 = No Change) Figure 7: Model 3 Results - Seasonality Removed
2.4.4 Model 4 - Seasonality Removed with Linear Regression (Installation only independent variable) Model 4's analysis procedure started by removing seasonality trends using R's decompose function, similar to Model 3's process. Linear regression was then used to predict the monthly consumption with the installation of the HEM device being the only independent variable in the model.
T= f 1 x+E T - Monthly consumption after seasonalityremoved, fl, - IndividualHome Coefficient for effect of HEM install, x - Binary variable (0 or 1) representing if the HEM has been installed, E - Random error
With one binary factor that is used in the model to predict overall household power use, it should be no surprise that the linear regression model performed poorly. The model returned an R-Squared of 0.13 indicating the goodness of the fit of the linear regression is extremely low. This was expected - trying to predict human behavior based on only one independent prediction variable and a random error term would oversimplify this complexity of how people use power in their homes. Although the model performed poorly in its predicting power, it still provides insight in to the effect of the HEM within the distribution of households. Aggregating the results of the individual linear regression models showed an average reduction of 2.7% (i.e. the average p within the model was -0.027) with a standard deviation of 26%. After removing the extremes and being left with +/- 2a from the mean, the average
27 decrease of consumption after the HEM installation was 3.1%. Reference Figure 8 below for the distribution of results.
All Data (146 Homes) +/- 2afrom Mean (138 Homes) Decreased Usage ncreased Usage Decreased Usage Increased Usage 83Homes M*63 Homes 79 Homes 59 Homes
Avg - 2.7% Decrease Avg - 3.1% Decrease
LI.
-ib 45 0.0 -O -. 2 Relative Change in Electricity Consumption Before and After HEM Installation (0.0 = No Change) Figure 8: Model 4 Results - Linear Regression (Install Only Variable) with Seasonality Removed
Looking at the group homes where the installation variable was significant (pStat <0.05) narrows in on the potential for what is likely to be the highest biased set of users - that is the electric customers that were potentially the most engaged with their home energy monitor feedback. The installation variable was significant for 72 of the 146 homes. Within this subset of 72 homes where the installation of HEM was a significant factor, the average reduction was 4.6%. Removing the extreme values (outside of +/- 2a from the mean), that we can confidently say the power changes were not caused by the HEM, the resulting average consumption decreased by 5.9%. Reference Figure 9 below for a focused subset of Model 4's results.
All Homes with Significant Installation Factor +/- 2a from Mean (55 (63 Homes) Homes) nreased Usa"2 a Decrnase4 " 5 increased usage sageincreased Usa rge 39 Homes 24 Homes 35 Homes 20 Homes
Avg - 4.6% Decrease Avg - 5.9% Decrease U wC 0~ a) LL
r- -
-1, -0'5 0.0 04 t.o ' (10.2 Relative Change in Electricity Consumption Before and After HEM Installation (0.0 = No Change)
Figure 9: Model 4 Focused Subset Results - Significant InstallationFactor Homes Only
28 2.4.5 Model 5 - Seasonality Removed with Linear Regression+ (HDD, CDD, Installation independent variables)
Model 5's analysis removed the seasonality trend using R's decomposition function, similar to
Models 3 and 4. Linear regression modeled monthly consumption as the dependent variable with three independent variables - HEM installation (binary), monthly Heating Degree Days (HDDs) and Cooling Degree Days (CDDs) for each home.
T = fl1x + fz H + Ws + E T - Monthly consumption after seasonality removed, P, - Coefficients of the effect, x - Binary variable (0 or 1) representingif the HEM had been installed, H - HDDs in the state the given month, C - CDDs in the state the given month, - - Random error
Including HDDs and CDDs in the model is a method of controlling for weather. For details associated with HDDs and CDDs, reference Section 2.2. The goodness of fit of Model 5 was slightly higher than Model 4 (which had the installation being the sole independent variable). Model 5's average R-Squared for the set of households was 0.19, which still shows the model has a poor predicting power. Similar to Model
4's low prediction power, even though this model would not be a strong candidate to predict individual home energy usage accurately, it can still provide insight into the effect of the HEM installation based on the coefficient's trend and significance. However, limitations and improvements of the model are discussed in the Section 2.6. The results of the Model 5's analysis of 146 homes showed an average reduction of 2.0% (i.e. the average Pi within the model was -0.020) with a standard deviation of 26%. After removing the extremes and being left with +/- 2cy from the mean, the average decrease of consumption after HEM installation was 2.9%. Reference Figure 10 below for a distribution of Model 5's results.
29 All Data (146 Homes) +/- 2c from Mean (137 Homes) Decreased Usage Increased Usage Decreased Usage Increased Usage 83Homes 63 Homes 79 Homes 58 Homes
S- .Avg - 2.0% Decrease (. Avg - 2.9% Dec rease C a a U
.10 00 10 OA 020s 0.0 O2 OA Relative Change in Electricity Consumption Before and After HEM Installation (0.0 = No Change) Figure 10: Model 5 Results - Linear Regression' (Installation,HDD, and CDD variables) with SeasonalityRemoved
Focusing in on the homes where the installation variable was significant (pStat < 0.05) provided even greater decreases in consumption. The installation variable was significant for 62 of the 146 homes. Within this subset of 62 homes where the installation of HEM was a significant factor, the average reduction was 4.4%. Removing the extreme values outside the bounds of +/- 2a from the mean, the resulting average consumption decreased by 5.8%. Again, this focused subset of users is likely to be a highly-biased subset of users. Reference Figure 11 below for a distribution of this focused subset of Model 5's results.
All Homes with Significant Installation Factor +/- 2a from Mean (54 Homes) (62 Home +-2 rmMen(4Hms Decreased Usage Increased Usage Decreased Usage Increased Usage 38 Homes 24 Homes 34 Homes 20 Homes
Avg - 4.4% Decrease Avg - 5.8% Decrease Av-.4 eces
U_ L...... --o r _rr ..... I... .1--- 1.0 0 0.0 0.5 1.0 .0,4 -02 00 02 04 Relative Change in Electricity Consumption Before and After HEM Installation (0.0 = No Change)
Figure 11: Model 5 Focused Subset Results - SignificantInstallation FactorHomes Only
30 2.4.6 Model 6 - Consistent Time Frame Comparison
Model 6 removed seasonality data using R's seasonal decomposition pattern recognition
software. After seasonality was removed, each month's usage was normalized based on its monthly usage factor, similar to the method used in Model 2, except seasonality had been removed prior to normalization.
After normalization, the usage before and after the HEM installation was compared on a consistent time frame - ten months before, and three months after HEM was installed. This was a primary difference between Model 6 and other models.
Three months after installation was chosen as a minimum for this model for multiple reasons. First, it takes time for the HEM monitor to identify devices within the home. It is assumed that the effect of the HEM for reducing energy consumption was based on the appliance-level feedback. A single month after installation the HEM would not provide the level of details for appliance usage as two or three months after. Therefore, it was assumed that the second and third month would be a better indicator of the longer-term effects of the HEM.
Along those lines, there were multiple homes within the dataset that did not meet the minimum data on either side of the installation (i.e. a home many have had only 9 months of data before the HEM was installed or the data was only available for two months after installation).
Applying this filter resulted in 130 homes meeting the time frame requirements. Increasing the amount past three months would have reduced the amount of homes we could have included in the analysis further.
The 130 homes that met the three-month minimum time frame for installation showed an overall reduction in energy consumption. The average effect of the HEM installation was a reduction of 3.1%. Focusing in on the +/- 2a from the mean, the average decrease of consumption after the HEM installation was 2.6%. Figure 12 below provides a distribution of Model 6's results.
31 All Data (130 Homes) +/- 2; from Mean (122 Homes) Decreased Usage Increased Usage Decreased Usage 4 Increased Usage 73 Homes 57 Homes 68 Homes 54 Homes
Avg- 3.1% Decrease U V - Avg - 2.6% Decrease
LA.
F - __ __ _
-0.8- -2 0.0 02 0.4 016 O' -00 2 -0300 0.1 02 0.3 Relative Change in Electricity Consumption Before and After HEM Installation (0.0 = No Change) Figure 12: Model 6 Results - Consistent time frame evaluation with Seasonality Removed and monthly normalization
2.5 Summary of Results
As described above, there are a number of options for testing the effect of energy reduction. There is no "correct" way to analyze the data, however, providing a spectrum of methods allows a realistic view on the variability an analysis method can have on results. In all analysis cases with seasonality patterns taken into account, the Home Energy Monitor installation was linked to a decrease in consumption. Averaging the results of across each of these analysis methods gives a 2.3% reduction after HEM installation, which is including all results accept the simple average. Removing the outliers and only looking at results within +/- 2a from the mean shows a 2.6% - 3.1% reduction. Table 2 provides the results from each method.
Model Description Results Results (+/- 2a)
1 Simple Average +1.7% +0.4%
2 Monthly Normalization -1.0% -2.6%
3 Seasonality Removed -2.7% -2.6% Seasonality Removed + Linear Regression 4 (Install Only Variable) -2.7% -3.1% Seasonality Removed + Linear Regression+ 5 (HDD, CDD, Install Variables) -2.0% -2.9%
6 Consistent Time (IOmo before, 3mo after) -3.1% -2.6% Table 2: Analysis Results Summary - Reduction of Household energy usage after NILM HEM installation
32 2.6 Limitations and Recommendations In this section, we will provide a list of limitations and critiques of this energy efficiency analysis and recommendations for improvement for similar analysis that may be conducted in the future. There are multiple concerns detailed below that may have had a positive impact on the results, while a number of the concerns may have had a negative impact.
No control group to compare energy savings - As this is a time-based study, the residential usage before the HEM installation was the "control group" for testing of the hypothesis. The homes in the "test group" were the same as the control, but after the HEM was installed. A potential improvement would be to find similar homes that do not have a NILM device to be compared as a control. A key attribute to show the energy usage difference between the control and the test group would be to match homes that have similar historic usage patterns and geographic location. This direct comparison of similar homes in a nearby geographic area could remove weather bias and provide stronger evidence of the effect of what is attempting to be tested (in this instance, NILM's effect on energy efficiency). In addition, a properly chosen control group could remove the need to normalize for seasonality and weather since the homes would see the same patterns.
Biased sample - The HEM customers that were a part of this research were likely all "energy- enthusiast" consumers that opted-in to obtaining a NILM device. That is, these customers sought out the purchase of the NILM device, installed the device themselves in their breaker panel (which requires a knowledge of electricity) or potentially paid to have it installed by a professional electrician. These NILM owners are not the average residential electricity customer. The biased sample could show the associated energy reduction results may be greater than what would occur in the general population of electricity customers. There is also the likelihood that these energy-conscious customers were making efficient changes to their home prior to the NILM installation. This would be an argument that the introduction of NILM to less energy-conscious individuals could result in a greater reduction of energy usage. It is uncertain which side of the "opt-in bias" has a greater effect on the results of this study, which is an additional limitation.
33 Seasonality Removal Error - A number of analysis methods described above are dependent on R's seasonality decomposition software (Models 3 - 6). Recognizing seasonal patterns for a single home's energy usage year-after-year, and removing that pattern, may have a larger effect than the Home Energy Monitor itself. Although a necessary step to attempt to remove seasonal patterns, it would be ideal to ensure each home is modeled next to a control group of similar homes to verify energy reductions. It is also possible to aggregate a number of homes' energy usage in the seasonality decomposition step. This could be completed in a controlled experiment where homes in the same zip code install the HEM on the same date (or close to the same date) and their usage information is summed together. For our analysis, zip codes and the date installed did not match between houses for the aggregation to occur.
Sustainability of Savings - The average timeframe the test group had the HEM installed was seven months at the time the study was conducted. A future study could confirm the sustained effect years into the future with continued engagement of the customer. This would confirm customers taking action to reduce their electricity consumption have not fallen back into the prior usage trends.
No direct energy efficiency feedback was provided to the customers - The NILM device installed came with an application showing the usage in kilowatt hours (kWh) and also the cost associated with that usage for each appliance. There was no additional feedback to alter customers' behavior to reduce energy usage. It has been shown that adding gamification and specific actionable feedback can lead to a greater reduction in consumption.
Passive NILM device - There was no control over individual appliances through the NILM feedback application. In order to take action on the disaggregate information they received, customers would need to take an additional step to reduce their usage by changing a setting on an appliance or adjusting their behavior in some manner. It is assumed that the magnitude of energy reduction will increase as more control is integrated into the HEM feedback mechanism. For example, the application would recognize an outlier day that the air conditioner was running nearly all day (on an extremely hot day). The app would then send a notification stating the AC was running 90% of the time yesterday and ask the user if they would like to increase the
34 temperature of their home during the time they are not home, which the user could provide feedback on timing and set the temperature within the application.
Small sample - A larger sample size of residential electricity customers with a non-intrusive load monitor installed would have made this result much more statistically significant.
Statewide weather data - A simplified method for determining the weather was used to model and account for the months requiring energy to heat or cool the home. Using the nearest weather station to the house zip code to obtain CDD and HDD information would be a more accurate method of incorporating the weather into the analysis.
Shipping address match for electricity usage - The algorithm used to pair the historic monthly power usage with the individuals who had a HEM installed used a shipping address as an input. It was assumed that the shipping address where the HEM was delivered was the location where the HEM was installed. This could be an incorrect assumption for a portion of customers who may have had the monitor shipped to their work location or gave it as a gift to another household. It is assumed that this scenario is the outlier case and was not a large factor.
Pattern recognition of individuals - It is difficult to model individual behavior, which is what each of the models described above attempts to do. Ideally, the usage of individuals who installed a non-intrusive load monitor at the same time could be aggregated together. As individuals involved in this study did not have a specific installation date, but rather had a random distribution of dates installed, aggregating households on their installation date was not possible.
2.7 Policy Recommendation There are a number of federal and state government programs that sponsor energy efficient devices. These include ENERGYSTAR rated products which have been verified to have a reduced energy consumption from their standard energy using peers. With a wide variety of rebate options, ENERGYSTAR appliances and products are often sought out by customers for their reduced energy demand and also their rebate incentives.
35 One such ENERGYSTAR rated product is the Honeywell Lyric RoundTM Wi-Fi Thermostat. With a retail value of the Honeywell Thermostat is $199 (25), the Massachusetts energy efficiency program MassSave, provides a reduced price of $50 ($149 rebate) on the thermostat plus free installation with a Home Energy Assessment (26). The basis for this rebate offer is the energy consumption savings associated with the thermostat when these types of thermostats have a wide-spread adoption. We will take a closer look at linking the rebate to the known energy savings and recommend a similar rebate for NILM devices based on the efficiency study conducted above and the specific studies mentioned in the literature review. Based on a study performed by Honeywell on over 34,000 of their Wi-Fi connected thermostats across the United States, the average total heating and cooling savings fell between 6.7% and 9.1% (27). In New England, only the space heating savings associated with the thermostat installation were estimated in the study, therefore we will use the national average as an approximation for Massachusetts / New England. According to the 2009 EIA RECS Survey, heating and cooling represented 47% of the total United States residential energy usage, and 60% in Massachusetts (28). Assuming Massachusetts would result in a similar heating and cooling savings rate of 7.9% (average of two Honeywell studies), and this accounts for approximately 60% of the total household energy use, installation of a programmable Honeywell Thermostat can be linked to an expected savings of approximately 4.7% of the total energy bill in Massachusetts. A $149 rebate on reducing the household energy by 4.7% is approximately $32 for every 1% of energy saved within the home. This rebate ratio can be linked to the savings associated with the HEM studied in this paper (2.3% reduction). A $74 rebate could be justified for the customers who opt-in to purchase this particular HEM. If this study is included in the prior energy efficiency studies (Section 1.4.1), weighted by the number of households involved in the study, an average household energy reduction of 4.8%. This would support a $153 rebate to be equivalent to the energy savings rebate of the Honeywell WiFi thermostat. With this information, it recommended a minimum $100 rebate offer can be justified for energy savings. This is not factoring in the additional targeted demand response benefits associated with the installation of the devices described in Chapter 1I1. As an additional incentive, offering a free installation of the HEM for individuals who apply for the rebate would be in line with the thermostat offer. A licensed electrician is
36 recommended for both the installation of the HEM device as well as the installation of the Wi-Fi thermostat. An additional benefit, which is discussed in more detail in Ciapter IV, the NILM device could be an energy efficiency validator of ENERGYSTAR products. Customers will gain the understanding of the how an appliance is performing within their home specifically rather than the current generic statements of how it performs in general via the label on the device at purchase. These energy efficiency labels are generally performed in a controlled environment on what an average yearly energy consumption for the appliance would be. Although difficult to quantify from a policy incentive standpoint, the additional benefit should be considered. As previously discussed, NILM technologies have been available for many years, these monitors within homes are extremely uncommon. Although no studies have quantified how many homes have these devices installed, it believed to be below 1% of the population in the United States. Even amongst the energy enthusiast communities, NILM devices are rare to find in homes. Compared to the 41% of the U.S. population that has a programmable thermostat installed in their homes (29), NILM device installations have a lot of space for growth for promoting energy efficiency and reducing individual's carbon footprint. The proper government incentive structure for NILM devices is an opportunity for growth.
37 Chapter 3 - Targeted Demand Response
3.1 Introduction
Individual consumer behavior is generally unpredictable. However, given the right incentives, consistent trends of economic supply and demand are followed by consumers. That is, as the price increases, the demand will decrease - or as incentives increase, behavior adjusts to attain the incentive. Demand response (DR) in one method of using the theory of supply and demand for incentivizing electricity customers to reduce their usage around the power grid's most critical hours. DR has a great potential for reducing transmission and distribution costs as well as emissions from the peak generating plants. The installation of NILM devices can play a critical role within DR. As previously shown, feedback provided with the help of NILM devices can be linked to a reduction in overall energy usage. Not only can customers understand their energy use and time of use by engaging with a NILM device, but utilities can also make their DR programs more effective. If a utility can understand their customers' usage in more detail, targeted feedback with specific incentives for reduction can be achieved. DR is focused on energy reduction within a specific time-of-use. Although the current standard billing method for electricity is one price per kWh consumed, the cost of which the electricity is generated ranges widely over the course of the day. In general, the highest cost and highest emitting electricity generation is during what is known as "peak hours". DR is the focus of providing incentives to decrease electricity use during these peak times. These generation prices vary throughout the time of the day depending on the amount demanded by customers. For an example of a single summer day see Figure 13 below. The peak-hours on this day would fall between 4PM and 7PM, where the price of electricity spikes to over $1 00/kWh. Reducing the amount of electricity demand during these peak hours will have a greater benefit to reducing costs for the utility.
38 Real Time Pricing New England ISO - July 18, 2017
250
200
.M100 MUso
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of Day
Figure 13: Price of Electricity (July 18, 2017) (30)
There are many different types of DR programs. As one DR program example, customers are notified to reduce their energy consumption for a few hours on what is called a "peak event". The customer is generally incentivized for reducing their consumption during these peak hours, and it can lead to significant energy reductions. NILM devices have the opportunity to give very specific appliance usage feedback and allow for customers to have the greatest effect on a DR peak-event. DR is a lower risk option that many alternatives for changing behavior, including time-of-use (TOU) rates and critical peak pricing. NILM devices can ultimately be a step towards real-time pricing (RTP) which is the ultimate form of demand response, passing the cost of electricity directly to the customer as the utility buys it throughout the day. This ideal form of economic incentives may not be possible without a significant increase in the amount of automatic optimization in the home. However, the appliance usage data associated with NILM devices can be used to understand how to automate appliances with limited effect on the comfort level of the home. Both utilities and customers can benefit from the sharing of this information. With a reduced peak load, utilities can also reduce their spending on increasing their capacity infrastructure. This savings would then be passed down to the customer with targeted incentive programs for the specific alteration of their behavior. There are existing DR programs deployed throughout the country by private companies and utilities. However, the method of analysis described in this section is a new way to look at the potential each household has based on their appliance usage data.
39 This section will elaborate on a method of evaluating disaggregate data of specific appliances in order to determine their potential for demand flexibility. Depending on the deferral potential of the appliance, the higher the DR incentive potential. In addition, there is a method for targeting households with the highest demand response potential based on this demand flexibility metric.
3.2 Data for Study The appliance usage data used for this research was obtained from 53 homes throughout New England. These specific customers have both solar panels on their home and a HEM device installed. Energy consumption of each appliance of interest was captured on a minute-by-minute scale. The average power consumption each appliance used each minute allowed for a consistent evaluation metric to be used to compare appliances and households The household appliances that were considered as a part of the deferral potential and available within the dataset were the refrigerator, ice maker, dishwasher, air conditioner, dryer, hot water heater, vacuum, and electric vehicle. These specific appliances were chosen as there is typically not customer requirements associated for when these items have to be using power. Rather, the customer requirement is the eventual result of the appliance power usage. That is, the central air conditioner can turn on and off as it requires, as long as it maintains a certain temperature range for the household. Similar to the hot water heater, and the fridge, these appliances need to maintain a certain outcome temperature. The potential to defer the electric dryer, vacuum and electric vehicle is a little more nuanced, however, a similar deferrable principle applies. Incentivized correctly a shift in the usage of the electric dryer can still have the same customer satisfaction for the result of the electric dryer - that is dry clothes at the time they are needed. It should be noted that within the data set there was only one electric vehicle that was included. Reference Figure 14 for a distribution of the household locations included in the study. It should be noted that within the 53 homes, there was only one electric vehicle in the dataset. It was included as a referenced point, but it not necessarily a representation of all electric vehicles deferral potential.
40 Key (Monitors per zip code) I1 Monitor .2 * 3 Monitors
Figure 14: Locations of Homes in DisaggregationStudy
3.3 Quantified Flexibility Potential The ability to quantify the demand flexibility potential of an appliance is described in this section. This is a similar method of classification used by Su, et.al. in their paper "An Appliance Classification Method for Residential Appliance Scheduling" (31).
3.3.1 General Appliance Deferral Potential This metric of appliance deferral potential can be used to calculate how easily an appliance's time of use can be altered, along with how valuable that shift is for reducing power. This specific metric can be used to understand the appliance deferral throughout the day. It can be a guide for utility planners looking at how to evaluate demand response of different appliances.
Analysis Method In order to capture the demand response potential of an appliance or a home, the power each appliance uses needs to be combined with how easily that appliance can shift consumption. The calculation for the average power consumed was multiplied by logarithm of the time a ratio of ability to shift. The Toff to Ton ratio represents an amount that the appliance could shift relative to
41 the time spent on multiplied by the quantity of uses on average per day. For a simple example of the deferrable load potential of a refrigerator, see Figure 15. T Deferral PotentialAppliance = PAVG X UsageDailyAVG X 10g(o ) Ton
PAvG - Average Power, Tff - Time appliance is off, T,, - Time appliance is on
200 TON TOFF 180 160 S140
120 * 100 PAVG 0
40
20 0 ------1 51 101 151 201 251 301 351 401 451 501 551 601 651 701 Time (min) Figure 15: Refrigerator'sPower Cycle. After a briefspike at the onset to turn the motor on, the fridge power draw is relatively consistent each time it turns on until the interiorof thefridge reaches the temperature threshold set by the user. This is a "simple "power cycle that shows the variables in the deferrablepotential equation with each peak representingone cycle.
Results Using this deferrable potential calculation each appliance identified by the HEM has its own unique score. The one electric vehicle that was included in the dataset had the highest average power consumption for each cycle, followed by the hot water heater, air conditioner and dryer. The vacuum had the highest ratio of time off vs time on, followed by the ice machine, dryer, hot water heater, and electric vehicle. The highest quantity of daily uses was the refrigerator, with the air conditioner, hot water heater, and dishwasher rounding out the top four. According to this metric, the top potential deferrable appliance is the hot water heater, based on being in the top four of each term of the metric. The air conditioner, electric vehicle, and dryer have a high deferrable potential based on this quantifying metric as well. Figure 16 shows a summary of the appliances with the highest power, off/on ratio, and daily usage. For each appliance's average score across the dataset, see Table 3.
42 Top Power appliances GD EV, Hot Water Heater, AC, Dryer Top aplianes:Topfflo rato Deferrable Appliances Top off/on ratio appliances: HWH, AC, EV, Dryer Vacuum, ice Machine, Dryer, HWH, EV H (! Top Daily Use appliances: Fridge, AC, HWH, Dishwasher
Figure 16: Summary of Top DeferrableAppliances
Deferrable Normalized Deferrable Avg Power Appliance Potential Potential (kilowatts) Off/On Ratio Daily Use Hot Water Heater 65.4 1.00 3.42 80 6.7 Air Conditioner 23.8 0.36 1.51 26 22.0 Electric Vehicle 7.2 0.11 6.92 76 0.2 Dryer 6.3 0.10 1.21 136 1.1 Vacuum 4.3 0.07 0.84 570 1.1 Dishwasher 3.9 0.06 0.29 68 4.1 Refrigerator 2.2 0.03 0.14 4 28.2 Ice Machine 0.3 0.004 0.02 343 3.0
Table 3: Deferrable Potentialof Appliances
3.3.2 Peak Demand Flexibility With a greater adoption of NILM devices in combination with sharing the appliance usage data with utilities, DR programs will become more and more effective. The information on this small subset of homes can be extracted to a number of homes to investigate the potential benefit associated with deploying and incentivizing a higher adoption of "smart" hot water heaters. Based on the metric reviewed in Section 3.3.1 the deferrable potential of the hot water heater is 2.5 times higher than the air conditioner. This provides a single metric to compare the flexibility associated with shifting the hot water heater demand throughout the day. The purpose of this section is to iterate on the previously developed metric to understand the deferral potential within the peak event time frames during of the year.
Analysis Method The metric has been slightly modified to incorporate the usage within peak demand:
43 Toff Peak Deferral PotentialAppliance = PAVG x PU X 10g(-) on PAVG - Average Power, PU - Peak Usage Factor(% of time appliance is on), Tff - Time appliance is off To, - Time appliance is on
The peak usage factor was determined by first looking at state-wide consumption of electricity for Massachusetts, Rhode Island, and New York during the summer of 2016. Each state had five days that were the highest "peak usage" for that season. It is just as feasible to run this analysis for a single day, 10-days, or the entire summer peak season to understand the breakdown of appliance usage and how the factor changes. For this analysis, five days was an appropriate number of days the utility may call a demand response event. The top five-hours of electricity consumption of summer 2016, where electricity consumption in each state reached its highest level, were used to filter the appliance usage results. The appliance minute-by-minute usage was filtered into three-hour peak-usage windows around those top usage events. Each home had a breakdown of what appliances were in use during these peak hours. These appliance usage percentage during each of these timeframes was used to calculate the peak usage factor.
Results The air conditioner had the highest peak deferrable potential out of any of the appliances studied, which was driven by its 16.2% peak usage factor. The hot water heater remained very close to the air conditioner in deferrable potential due to its high average power consumption and timing flexibility. This information can benefit utilities looking to expand their DR program outside of the space heating and cooling area of residential homes. The results of the peak deferral potential are summarized in Figure 17, with detailed numbers provided in Table 4.
Top Power appliances Hot Water Heater, AC, Dryer
Top offlon ratio appliances: Top Peak Def Appliances Vacuum, ice Machine, Dryer, HWH AC, HWH, Dryer ( Top Peak Use appliances: AC, HWH, Fridge, Dryer
Figure 17: Peak Usage Appliance Deferrable Summary
44 Deferrable Normalized Deferrable Avg Power Off/On Peak Usage Appliance Potential Potential (kilowatts) Ratio (%) Air Conditioner 34.8 1.000 1.508 26 16.2 Hot Water Heater 33.8 0.972 3.419 80 5.2 Dryer 2.6 0.074 1.207 136 1.0 Vacuum 1.2 0.033 0.841 570 0.5 Refrigerator 0.4 0.012 0.136 4 4.6 Dishwasher 0.2 0.005 0.294 68 0.3
Table 4: Peak DeferrablePotential ofAppliances
It is important to note that this is a small subset of appliances within the homes, and does not represent all appliances being used during the peak times. Also, the single electric vehicle within the dataset was not charged during the top peak hour timeframes that were looked at. Details associated with the hours of peak timing within each state can be found in the Appendix, Table 1.
3.3.3 Use of Peak Metric Terms
We will now look at how the different factors of the Peak Demand Flexibility metric can be used to determine a potential peak load shift for each appliance. July 19, 2017 was the only day that was in the top five peak loads days within all three states of interest (see Appendix - Table 1). A 5-min interval of the ISO-NE demand for this date in history is shown in Figure 18 below. As shown on the graph, there is a relatively "smooth peak" with the maximum power demand occurring at 5:55PM of 23,641MW. The peak load was relatively stable from the hours of 2PM - 8PM, reaching within 1% of the maximum load for the day between the hours of 4:50PM - 6:15PM. The goal of this section will be to determine the capability of specific appliances to shifting their usage outside of this shaded region do reduce peak demand by 1%.
45 ISO NE Demand (5-min Interval) July 19, 2017 Peak Time: 5:55PM Peak Load: 23461 MW
25000
15000 Within 1% of peak (4:50PM -6:15PM) 10"0
0 12:00 AM 6:00 AM 12-00 PM 6-00 PM 1200AM Time Figure 18: ISO-NE Electricity Demand (July 19, 2017) (32)
To determine which appliances will have a potential for the largest impact on the peak load, it is useful to look at both the power consumed as well as the flexibility of the appliances. Figure 19 below is a qualitative sample of how the air conditioner and the hot water heater have both a high flexibility and a high power used during peak demand.
Appliance Peak Power Use + Flexibility
30
.x20 HtWtrHae
10
:0 0.5 1 . 2 25S .
Figure 19: Potentialfor deferral based on metric score - the appliances that have a higher power used duringpeak and higherflexibility (off/on ratio) provide a largerpotential to reduce peak load. The shaded area represents the appliances worth consideringfor flexible demand response, while appliances outside of the shaded area would have minimal impact on reducing peak load.
46 With the lowest off/on ratio, the refrigerator would not be an ideal candidate for peak demand flexibility as the ability for it to shift its time of use is limited, as well as consuming a small amount of power during peak hours (relative to the other appliances studied). The dishwasher, dryer, and vacuum have a higher potential flexibility, but do not use a high portion of the home's power during peak. The ideal appliance candidates are the hot water heater and the air conditioner which have a higher power demand during peak hours, and also have the flexibility to shift their use to another time. The air conditioner and the hot water heater are two of the top candidates for evaluating peak demand potential in this model. Based on the air conditioner and the electric hot water heater having the highest flexible deferral score out of any of the other appliances, we will focus the effect of having these appliances shifted to run outside of the peak demand hours, with the ultimate goal to reduce the peak load by a minimum of 1%. For a load curve that is generally flat near the peak, the appliances with the larger "flexibility" measure would be the best target for deferral. That is, the hot water heater may be a better deferral option than the air conditioner since it could shift its usage to a greater extent. Although, this deviates from assumption that the air conditioners of New England were the primary driver of this peak demand on July 19, 2017 - which was one of the hottest days of the year at 93'F (in Boston, MA (33)). We will evaluate both deferral potentials assuming consumer willingness to shift is the same for both appliances during peak hours. In order to quantify the potential peak reduction by shifting the electric hot water heater and air conditioner, we will need to approximate the usage of these appliances outside of our sample homes. Residential energy consumption accounts for 38% of all power consumed (2). We will use this as a rough approximation for the amount of residential power consumed during the peak hours of July 19, 2017. It is likely that the residential sector contributed more than 38% of the peak demand on this particular day and time, as it was one of the hottest days of the year, it is assumed residential electricity customers had their air conditioner turned-on high during the hottest times of the day. As a conservative estimate, we will use the average consumption for the day to be the same as the average portion of residential consumption throughout the year. For the hot water heater's peak load reduction potential, according to EIA's 2015 RECS survey, approximately 36% of homes in New England use electricity as their primary fuel for water heating, with the remaining 64% using some type of natural gas, propane or oil (34).
47 We will now incorporate the data from the 53 homes with a the NILM HEM installed along with the recent survey results given above. Based on our sample, the average electric hot water heater uses over 3.4 kW of power when turned on. Also, on average the hot water heater accounted for 5.2% of the power demand during the peak hours of the day. We will also need the average time on and time off of the hot water heaters to confirm the power demand can be shifted outside of the "flat" peak area. The average run time for all the hot water heaters in our dataset was 13 minutes, while the average time off was 1120 minutes. Similar to what was provided in the Section 3.3.1 and 3.3.2, taking a ratio of the time off and the time on gives a flexibility score of the appliance. With a high flexibility score (much greater than 10), and high average time off, it is assumed the power demand of the hot water heater can be deferred to another time outside of the peak with minimal effect on residential user preferences. We have run a sensitivity analysis for different rates of control and optimization around time-of use, with 0% being a lower bound and 100% being the upper bound. For a summary of results of the sensitivity analysis, see Figure 20. The air conditioner provides another large potential for reducing peak load. The RECS survey of 2015 for New England showed that 30% of the homes have central air conditioning (35). For the air conditioners within our dataset, the average run time was 23 minutes, while the average time off was 823 minutes. During the hours of peak load (i.e. the hottest time of the day), this average run-time would be considerably longer, as well as the time off shortened, which would ultimately reduce our flexibility of the air conditioner during peak. For simplicity, a pre-cooling option for running the A/C outside of the peak demand timing was assumed to be feasible for all homes. For our dataset of homes, the A/C made up 16.2% of the power demand during the peak hours. We have also run a sensitivity analysis of portion of the air conditioner market controlled to show the potential effects, with results shown in Figure 20. It is important to note that this is a rough approximation of the peak demand reduction potential. The residential portion of peak demand is probably higher than 38%, and the air conditioner's portion of this demand is certainly greater than 16.2%. The total peak demand reduction potential may not be the only concern for the utility investigating the DR effect. They may be concern about the "rebound effect" where the appliance power demand shifts outside of
48 peak and creates a new peak. It was assumed for this analysis that the demand shift occurs far enough outside of the peak demand timing for the rebound peak to be avoided. A summary of numbers used to calculate both the hot water heater and the air conditioners effect on peak load is given in Table 5. As previously mentioned, the sensitivity analysis with the full extremes of market place adoption, from 0 - 100%, is shown in Figure 20.
New England Demand Peak Demand (MW) 23,461 1% reduction (MW) 235 Residential Portion (MW) 38%
Hot Water Heater Demand Homes that use electric HWH 36% Portion of homes' peak load 5.2%
Air Conditioner Demand Homes that use A/C 30% Portion of homes' peak load 16.2%
Table 5: Summary of Peak Load Statistics (2015 RECS data andpeak day disaggregatedata of 53 homes studied)
3.0%
C 2.5% E 2.0%
CL 1.5% 0 o 1.0%
0.0% - 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Portion of Appliances Controlled
- Hot Water Heater - Air Conditioner
Combined (AC + HWH) -Goal (1% of peak)
Figure20: Sensitivity Analysis for New England Demand Reduction - Shifting Hot Water Heater and AC consumption based on NILM data of 53 NE homes and 2015 RECS data
49 3.4 Limitations and Recommendations In this section, we will provide a list of limitations and critiques of this deferral metric analysis and recommendations for improvement based on specific objectives.
Peak Demand Selection: Operational Savings - The five hours chosen for contributing to the Peak Demand Flexibility Metric were the time frames when the energy demand reached the highest in each state. This demand timing could focus on a different objective other than reducing the general state-wide peak demand. For instance, if a utility were focused around operational savings via capacity reduction, the metric's input of peak demand timing could be changed to the time a large portion of an area's distribution capacity is consumed. Depending on where the demand is distributed through the network, there could be areas of the grid with a large remaining distribution capacity while others are near maximum capacity for different times of the year. This would result in different timing (and locations) for the "peak usage" for each home, and therefore a different breakdown of the appliance use distribution within the household. For future pilot studies focused on operational savings of NILM, it is recommended to work with the utility distribution engineers to ensure areas of the grid that are expected to reach a high portion of the grid capacity will have a distribution of NILM monitors installed within the area.
Peak Demand Selection: Cost and Emissions Reductions - The Peak Demand Flexibility Metric could be motivated by the largest contributor to greenhouse gas emitters, "peaker" power plants. A peaker is a generation power plant that is turned on to serve the peak demand. It is often referred to as the "last to be turned on" based on its high cost and high emissions. The selection of time used for peak demand could be based around these peaker plants, which, in turn would result in a different distribution of appliances run in the home. The understanding of which appliances are being used during this time can provide utilities a different incentive structure than the general peak demand strategy.
No Measure of Customer Willingness to Change: The deferral metrics described above are missing a measurement of residential customers' willingness to shift or reduce their electricity
50 usage. For example, it seems likely that during the hottest portion of the summer days (typically lined up with the peak hours when residential customers are home) customers want their air conditioner running, but they won't mind their hot water heater staying off until the next morning when they want to take a shower. This could be counteracted with the use of higher incentives - at the right price, AC usage can be deferred, however, this incentive measure could be considered to improve the realistic potential of the deferral metrics. This is partially addressed with the sensitivity analysis portion of the metric usage, however, not in the details of the two- metrics provided.
Small Sample - With only 53 homes in the dataset, it is difficult to draw widespread conclusions and make the examples statistically significant.
Biased Selection - The 53 homes' usage analyzed had solar panels installed on their homes, which will represent was is likely to be a more affluent portion of a utility's customer base.
51 Chapter 4 - Additional NILM Use-Cases
4.1 NILM Benefits The value of a wide-spread adoption of NILM devices to residential electricity customers is not limited to energy reduction and targeted demand response. There are a number of additional benefits that are worth further investigation. These benefits are briefly summarized in this section and are recommended to be tested in future work between NILM manufacturers and utilities. There is significant overlap between stakeholders within all of these benefits as described below.
Validation of energy efficiency investments - Both utilities and governments incentivize customers to pursue energy efficiency purchases. Residential customers making these efficiency purchases will base their decision on what the sticker in the store tells them and then not be able to validate the results upon installation. This could be a win-win situation where the appliance upgrade is validated for the consumer, and the incentive offered for that appliance is validated with the utility / government sponsoring the reduction. This could result in manufacturers having more data on their performance in the field and updating their efficiency results, for better or worse.
Improved customer satisfaction - NILM technology on a wide-spread customer basis can be a positive change in the customer-utility relationship. This could come from a number of different methods including:
1. Elimination of high bill surprises with instantaneous usage feedback - possible to set budgets and automatically notify when usage reaches a certain portion of budget or have a continuous itemized bill of appliance electricity use. 2. Appliance fault detection - NILM has the large potential to notify a customer about a faulty appliance before it fails. 3. Awarding individuals for less energy use during critical peak hours - essentially acting as a smart-meter in this regard to have the utility understand time of use.
52 4. Appliance usage limits - If the kids in the house are playing too many video games, or if you want to reduce your television watching, this is a method to automatically monitor the amount of time an appliance is in use. 5. Home occupancy monitoring and security - A number of reasons for wanting to understand if someone is home or their activity level within the home can be further researched (monitoring of elderly health, home monitor while away, etc.).
Social awareness of carbonfootprint - In a larger context, there are many societal benefits for linking appliance usage with energy costs and potentially educating more people to link these usages with carbon emissions. This alone could have an effect on individual's behavior to shift their consumption of electricity outside of the peak times and ultimately mitigate some of the effects of climate change.
Reduced survey costs - Both utilities and governments are updating their models based on survey of homes for appliances and energy usage. For instance, the EIA's budget for 2018 decreased by over $3.2M with an example provided:
"Use of alternative data collection modes to increase operationalefficiency and integrate new data such as "behind the meter" measurements of electricity consumption by individual devices and appliancesfrom a representativesample of homes and businesses." (36)
Reduced operational costs - According to OPower, 42% of all calls are bill-related. With active "high-bill" alerts, high bill call volume for a utility was reduced by 19% (37). This is a large cost for the utility which is passed on to the electric customer for maintaining operations.
Improved demand prediction and simulation models - As forecasting demand is the basis for operational decisions being made at the utility, getting more of a "ground-up" approach for how individual homes are using their appliances, what factors are critical in their time-of-use, and how to properly model that within the forecast can be advantageous.
53 New revenue streams - Provided privacy concerns are addressed, individuals that could save the most with an efficiency upgrade of an appliance could be notified of the benefits and the potential payback window with the upgrade. For instance, if an individual has incandescent lightbulbs that are consuming three times the power of an LED, the NILM device could act as a virtual auditor to recommend an upgrade to the LED lightbulb with a direct link to a purchase.
4.2 Risks and Mitigation With each of these potential benefits comes a concern over customer privacy and security. As more home energy monitors are purchased by utilities and distributed to consumers, handling the data for confidentiality is important to maintain customer trust. This information includes when people are present in the home, watching television, cooking, using the bathroom, sleeping, and any other behavior that can be linked to electricity usage. It is of the opinion of the author that any privacy concerns associated with home energy monitors can be overcome with proper handling of information, and also more wide-spread information on the energy savings and emissions reduction benefits of the technology. This opinion is based on the fact that individuals are allowing more and more technology intrusion into their home that use cloud services. These services currently offer some efficiency benefits (voice recognition for turning on music, turning off the lights, and placing online orders), but are still giving large amounts of personal information to companies to use for targeted marketing. Each utility that will begin to offer NILM to customers should pair the offer with a marketing strategy describing the benefits that will be provided to customers upon NILM being introduced in their home. In addition, it should describe what their anonymous data will be used for (i.e. bill reduction, reduced carbon emissions, for the improvement of society). This could potentially be a linked to a social norm for the greater good. There are also a number of techniques that are available that will mask the data against cyberattacks and potential intrusion of privacy. These will not be reviewed in detail, but are referenced for interested parties (13) (38) (39).
54 Chapter 5 - Conclusions
5.1 Recommendations
5.1.1 Utilities The utility is a key player for promoting NILM adoption throughout the market. It is recommended that utilities continue to study and test NILM devices for benefits within their electricity customers. The following are recommendations for validating some of the benefits of interest for the electric utility.
Clear hypotheses to test - These hypotheses should be based on the most valuable (and most viable) use-cases with a control group to compare to. When looking at energy efficiency and demand response potential, a properly designed experiment can avoid some of the limitations specified in Sections 2.6 and 3.4.
Validate with ground-truth data - Ground-truth data could come in many forms, but installation of "smart-plugs" in a sample of households has been a consistent method in the industry. Similar to the Pecan Street study of EEme's disaggregated data, which was reviewed in Section 1.3.3, this ground truth data can determine how accurate the NILM device is at detecting appliances. It is likely that the higher the sample frequency of the NILM device, the greater the ability to identify devices based on their "load-signature", however the validation of this assumption is necessary.
5.1.2 NILM Manufacturers In 2016, the average monthly electricity energy bill in the United States was $113 (40). Even with an efficiency savings of 5% of the average bill, a $300 price tag on the NILM device would require over four years to pay for itself on energy efficiency alone. It is difficult to justify the purchase of this $300 hardware device, that can be difficult to installed, that does not pay for itself. The recommendations below are general across the field of NILM manufacturers.
Lower the price - This could come with a government incentive as described in Section 2.7 or by reducing the costs to reduce the price. The cost structure can be lowered by scaling the numbers
55 associated with production of the devices, or entering in to a different mode of hardware installation as described next.
NILM built into existing hardware - Install the NILM directly in to the meter, or replace the meter all together with the NILM device. This would take an accuracy certification for the NILM to meet standards of the meter. It's also possible to focus on having NILM devices installed in to new break panels for new construction or electrical upgrades.
Provide personalized userfeedback - Specific feedback has been shown to be more effective than general feedback. NILM manufacturers, who have information on how appliances are being used in the home, are the best positioned to provide specific actionable feedback. This will result in the highest benefit associated with targeted energy savings and demand response use-cases. This could come in the form of competition with neighbors or similar homes for reductions.
Provide control - Integrate with additional devices for control over appliances, but ensure the customer can use the NILM as a "one-stop-shop" for direct control over all devices in the household available. This could spur further customer adoption.
Set-and-forget automation - Once control of appliances can be achieved, the ability to set the appliances for their optimal usage should be out of the customer's hands. The ideal setting should be defaulted around pairing with the least disruption for the customer with the highest benefit for emissions reductions, cost savings, or peak load reduction for the utility.
Expansion to commercial applications - All buildings operated for commercial purposes have a business cost associated with them. With appliance level information businesses can make better total cost-investment decisions for running machines.
Improve the installation - Difficult installation in an electric panel is unlikely to be a wide- spread adoption even at a lower price point. Removing any components of an electrical panel should generally be done by a licensed professional and that requires an additional step for the customer to installing and using NILM.
56 5.2 Summary This thesis has provided details on the potential benefits associated with the adoption of NILM and obtaining the disaggregated appliance level energy-use information. Two specific benefits were reviewed in detail: overall household energy reduction and targeted demand response. The thesis has also provided a series of recommendations associated with the adoption of NILM and developing the technology. Installation of a NILM device can provide customers information to become more energy efficient. Within the research performed for this thesis, a study was conducted that looked at the electricity consumption of 174 homes that were using a passive NILM device. This NILM device provided immediate feedback on the power consumption for a portion of the home's appliances via a smart-phone application. The homes reduced their monthly energy consumption by an average of 2.6 - 3.1% after the NILM installation. In addition, the wide-spread adoption of NILM devices can provide electric utilities information to reduce carbon intensity via targeted demand response. Multiple metrics have been presented in this thesis to quantify the deferrable load potential of specific appliances and individual households. Utility operational cost savings and greater customer incentives can be linked to the use of these metrics.
57 Appendix
PeakTime MA PeakTime NY PeakTime RI 2017-06-12T18 2017-07-19T18 2017-06-12T17 2017-06-13T17 2017-08-02T15 2017-06-13T16 2017-07-19T18 2017-08-03T17 2017-07-19T17 2017-07-20T17 2017-09-25T17 2017-07-20T16 2017-08-22T18 2017-09-26T17 2017-07-21T16 Table 1: Peak Consumption Times in MA, NY, and RI
Deferrable Normalized Deferrable Peak Deferrable Nor nalized Peak Avg Power Off/On Appliance Potential Potential Potential Deferr able Potential (kilowatts) Ratio Daily Use Peak Usage (%) Hot Water Heater 65.4 1.00 33.8 0.97 3.419 80 6.7 5.2 Air Conditioner 23.8 0.36 34.8 1.00 1.508 26 22.0 16.2 Electric Vehicle 7.2 0.11 0.0 0.00 6.921 76 0.2 0.0 Dryer 6.3 0.10 2.6 0.07 1.207 136 1.1 1.0 Vacuum 4.3 0.07 1.2 0.03 0.841 570 1.1 0.5 Dishwasher 3.9 0.06 0.2 0.00 0.294 68 4.1 0.3 Refrigerator 2.2 0.03 0.4 0.01 0.136 4 28.2 4.6 Ice Machine 0.3 0.00 0.0 0.00 0.018 343 3.0 0.0 Table 2: Deferrable Potentialand PeakDeferrable PotentialScores of each appliance (sorted by deferrablepotential)
58 Bibliography
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