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PREDICTING RESIDENTIAL HEATING ENERGY CONSUMPTION and SAVINGS USING NEURAL NETWORK APPROACH Dissertation Submitted to the School

PREDICTING RESIDENTIAL HEATING ENERGY CONSUMPTION and SAVINGS USING NEURAL NETWORK APPROACH Dissertation Submitted to the School

PREDICTING RESIDENTIAL HEATING CONSUMPTION AND

SAVINGS USING NEURAL NETWORK APPROACH

Dissertation

Submitted to

The School of of the

UNIVERSITY OF DAYTON

In Partial Fulfillment of the Requirements for

The Degree of

Doctor of Philosophy in Engineering

By

Badr Ibrahim Al Tarhuni

Dayton, Ohio

May, 2019

PREDICTING RESIDENTIAL HEATING AND

SAVINGS USING NEURAL NETWORK APPROACH

Name: Al Tarhuni, Badr Ibrahim

APPROVED BY:

Kevin P. Hallinan, Ph.D. Robert J. Brecha, Ph.D. Advisory Committee Chairman Committee Member Professor Professor Department of

Andrew Chiasson, Ph.D., P.E. Jun- Ki. Choi, Ph.D. Committee Member Committee Member Assistant Professor Associate Professor Mechanical Engineering Mechanical Engineering

Robert J. Wilkens, Ph.D., P.E. Eddy M. Rojas, Ph.D., M.A., P.E. Associate Dean for Research and Innovation Dean, School of Engineering Professor School of Engineering

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ABSTRACT

PREDICTING RESIDENTIAL HEATING ENERGY CONSUMPTION AND

SAVINGS USING NEURAL NETWORK APPROACH

Name: Al Tarhuni, Badr Ibrahim University of Dayton

Advisor: Dr. Kevin P. Hallinan

Upgrading and replacing inefficient energy-consuming equipment in both the residential and commercial sectors offers a great investment opportunity, with significant impacts on economic, climate, and employment. Cost effective retrofits of residential could yield annual savings of approximately 30 percent in the United States. This obviously could reduce greenhouse gas emissions in the U.S. significantly. Further, investment in energy efficiency can create millions direct and indirect jobs throughout the economy for manufacturers and service providers that supply the building industry. Unfortunately, the prediction in savings, which has been generally based upon energy models, has been circumspect, with energy savings typically over- predicted. Investor confidence as a result can degrade. An enabler for this research is a collective grouping of over 500 residential buildings used for student housing owned by a

Midwestern U.S. university. These residences offer significant variation in size, ranging from a area of 715 to 2800 square feet, in age, ranging from the early 1900s to new

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construction, and energy effectiveness, the latter occurring mostly as a result of improvements made gradually over time to some residences over the past fifteen years.

The historical monthly and electricity energy consumption for these is available. Additionally, in the summer of 2015, energy and building data audits were completed on a total of 139 residences. Documented in these audits were the amount and type of insulation in the and , areas and types of , floor heights, maximum occupancy, appliance (, range, ) specifications, heating ventilation air-conditioning system specifications, domestic hot equipment specifications, and the presence of a . Finally, county auditor real estate information was relied upon to obtain detailed features of each residence, including the age of the , number of , floor area of each level, and total floor area.

Using this data, a data mining approach based upon an artificial neural network

(ANN) model was shown to be effective in estimating the annual heating energy savings from a variety of measures for a large number of houses for which energy characteristics are known and energy consumption data is available. In combination with cost models for implementation of the measures, the cost effectiveness of every measure considered for each residence was estimable. This preliminary study provides the starting point for the research presented here. With knowledge of the individual cost effectiveness of all measures within a collective grouping of residences, it becomes possible to adopt a strategy for energy reduction based upon a ‘worst to first’ methodology. The economic impact of adoption of this methodology is then determined using an economic-input-output (EIO) approach. Considering only those measures that are economically viable and extrapolating the results from this study to the entire Dayton region yields with the initial energy

iv efficiency investment of $26.1M can result in a total local economic impact of $41.2M (i.e. summation of direct, indirect, and induced) and additional economic impacts stemming from the annual energy savings of $2.21M for the lifetime of the considered EE measures.

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ACKNOWLEDGEMENTS

A special thanks to my family. Words can not express how grateful I am to parents,

Ibrahim Al Tarhuni and Khadijah Al Tarhuni, for all your support and continuous encouragement throughout my years of study. This accomplishment would not have been possible without them.

Furthermore, my greatest thanks go to my wife Rema Ammar who has provided me through moral and emotional support in my life and through the process of researching and writing this dissertation. I am deeply thankful to my three lovely children, Ibrahim,

Muath and Hala, who bring me love and joy.

I would like to thank my advisor Prof. Kevin Hallinan for the freedom you have given me to find my own path and the guidance and support you offered when needed. The to Prof. Hallinan office was always open whenever I ran into a trouble spot or had a question about my research or writing. I also want to thank my committee members, Prof.

Robert Brecha, Prof. Jun-Ki Choi and Prof. Andrew Chiasson for generously offering their time, support, and guidance.

Finally, my sincere appreciation goes to my country, Libya who funded the whole PhD programme.

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PREFACE

As the goal of this is to predict energy consumption of any residence and then ultimately energy savings from specific energy efficiency measures based upon actual residential building and energy data, it is essential to document that the building set has reasonable variation in characteristics that influence energy consumption.

The research described in this dissertation was done in partnership with my colleague Adel Naji. We both were responsible for collecting the data used in the dissertation. The neural network model that was used to predict the energy consumption in chapter 2 was developed by myself. The economic analysis Chapter 3 was done primarily by Adel Naji and myself, except for the section 3.4.4, which assisted by Prof. Jun-Ki Choi.

Some of the sections of the dissertation represent shared authorship between myself and

Adel Naji.

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TABLE OF CONTENTS

ABSTRACT ...... iii

ACKNOWLEDGEMENTS ...... vi

PREFACE ...... vii

LIST OF FIGURES ...... x

LIST OF TABLES ...... xii

CHAPTER 1: INTRODUCTION ...... 1

CHAPTER 2: PREDICTING RESIDENTIAL HEATING ENERGY

CONSUMPTION AND SAVINGS FROM KNOWN ENERGY

CHARACTERISTICS AND HISTORICAL ENERGY CONSUMPTION ...... 5

2.1 Abstract ...... 5

2.2 Background ...... 6

2.3 Methodology ...... 10

2.3.1 Building Data Set ...... 10

2.4 Identification of Characteristics Having Greatest Impact on Energy

Consumption Using a Random Forest Approach ...... 15

2.5 Artificial Neural Network Model for Predicting Heating Energy...... 18

2.6 Results and Discussion ...... 21

2.6.1 Predicting Energy Consumption ...... 21

2.6.2 Estimating Natural Gas Savings from Retrofit Specific Measures ...... 23

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2.7 Validating Savings Using a K-Nearest Neighbor Approach………………...…..29

2.8 Conclusions ...... 31

CHAPTER 3: DATA-BASED APPROACH FOR MOST COST EFFECTIVE

RESIDENTIAL ENERGY REDUCTION ...... 33

3.1 Abstract ...... 33

3.2 Background ...... 34

3.3 Developing a Single ANN Model for All Residences to Predict Heating

Consumption and Savings from Individual Energy Efficiency Measure ...... 36

3.4 Economic Analysis of Sequential Adoption of Most Cost Effective

EE Measures ...... 38

3.4.1 Prioritized Energy Savings Among the Aggregate Set of Residences ...... 38

3.4.2 Levelized Cost of Prioritized Investment in Energy Efficiency

Measures Among the Collection of Houses ...... 41

3.4.3 Levelized Cost of Prioritized Investment from Among All Measures

Among the Collection of Houses ...... 44

3.4.4 Community-Wide Economic Impacts of Worst to First

Investment Option ...... 47

3.5 Conclusions ...... 53

CHAPTER 4: CONCLUSIONS AND FUTURE WORK ...... 54

4.1 Conclusions ...... 54

4.2 Future Work ...... 55

BIBLIOGRAPHY ...... 56

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LIST OF FIGURES

Figure 1. The nature of the variance in the measured building geometrical characteristics: (a). Afloor; (b). Awall; (c). Awindow; and (d). Aattic...... 13

Figure 2. Building energy characteristics histograms for: (a). Rattic; (b).

Rwindow; (c). Rwall; (d). efficiency (%); (e). water heater energy factor;

(f). SEER (AC); (g). refrigerator energy factor; and (h). attic penetration area ...... 15

Figure 3. Characteristics that have high influence on energy consumption ...... 18

Figure 4. The actual natural gas consumption plotted against the predicted natural gas consumption for a random sampling of houses for the heating season (October through April) ...... 22

Figure 5. Histograms of percentage gas consumption savings across all houses from each of the individual measure during winter season for:

(a). insulation; (b). attic insulation; (c). upgrades;

(d). furnace efficiency upgrades (%); and (e). water heater energy factor upgrades ...... 27

Figure 6. Histogram of percentage gas consumption savings from among all houses from all energy efficiency measures during winter season for: (a). envelope upgrade; (b). envelope and furnace upgrade; and (c). envelope, furnace, and water heater upgrade ...... 29

Figure 7. Comparison of predicted natural gas usage by ANN model with nearest neighbor method during winter season...... 31

x

Figure 8. The actual natural gas consumption plotted against the predicted natural gas consumption for a random sampling of houses for the heating season (October through April) ...... 37

Figure 9. The annual energy savings in million Btu for various measures vs. average levelized cost savings ($/ mmBTU) for all residences ...... 41

Figure 10. Histograms of energy savings measures for the levelized cost of energy savings ($/mmBTU) across all houses for:

(a). wall insulation; (b). attic insulation; (c). window upgrades;

(d). furnace upgrades;and (e). water heater upgrades ...... 42

Figure 11. Levelized cost of savings for houses presented in ascending order of cost effectiveness for the houses during winter season ...... 43

Figure 12. The aggregate levelized cost of fuel savings (LCFS) versus % of gas fuel savings for improvements for all measures considered ...... 46

Figure 13. Provides further detail about how the energy reduction is achieved at various levels using this ‘worst-to-first’ approach ...... 47

Figure 14. Breakdown of the local economic impact by EE investment ...... 51

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LIST OF TABLES

Table 1. Residential building geometrical and energy data and range of values made during the summer 2015 audit of 139 houses ...... 12

Table 2. Predictors for gas consumption ranked by influence on energy consumption ...... 17

Table 3. The inputs data used in ANN model constriction ...... 19

Table 4. Sample input data used in ANN model constriction...... 20

Table 5. The results of the t-tests for actual vs. predicted ...... 21

Table 6. The upgraded data used in ANN model constriction ...... 23

Table 7. Average gas consumption and percentage savings from individual retrofit measures...... 26

Table 8. A comparison between the collective groupings of measures savings ...... 28

Table 9. Predictors used in ANN model to predict energy consumption in all houses ...... 36

Table 10 Cost per square feet for improvements, and heating equipment replacement...... 40

Table 11. Savings and investment for achieving LCFS

$14/mmBTU from EE measures ...... 48

Table 12. Top 10 sectors out of several industry sectors benefitting from wall improvement to local industries ...... 50

Table 13. The direct, indirect, induced economic impacts of EE installation to the community ...... 52

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CHAPTER 1

INTRODUCTION

The Intergovernmental Panel on Climate Change (IPCC) in its most recent report documents that the human impact on the climate system is clear. Greenhouse gases emitted into the atmosphere can cause additional warming and lasting changes in the ecosystems, increasing the likelihood of severe, pervasive and irreversible impacts on humans and environment. Mitigation of climate change needs a substantial and sustained reduction in greenhouse gas emissions. Continued significant greenhouse gas emissions reductions in the coming decades can reduce climate risks in the 21st century and beyond, raising the likelihood for effective adaptation, which reduces the costs and challenges of long-term mitigation and contributes to climate resilient pathways for sustainable development.

However, civilization needs substantial emissions reductions over the following couple of decades and close to zero emissions of Dioxide (CO2) and other long-lived greenhouse gases by the end of the century, in a best-case-scenario to limit global warming to below 2°C relative to pre-industrial levels [1].

The U.S. Energy Information Agency (EIA) in its most recent report documents that 47.6% of energy produced in the U.S. is used to and buildings. The buildings sector consume for nearly seventy-five percent (74.9%) of all electricity produced in the U.S. and was responsible for nearly half (44.6%) of U.S. CO2 emissions in

2010 [2]. Fortunately, the cost effectiveness of Energy Efficiency (EE) is indisputable.

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A recent report by the American Council for an Energy Efficient Economy estimates the cost of EE at 2.8 cents/kWh. This cost is well below the cost of electricity nationally, which now is about 11 cents/kWh on average in the United States [3]. Further, the Deutsche Bank and the Rockefeller estimate a $279 billion US market for energy-efficiency retrofits – right now [4]. The economic potential of energy efficiency is strong. Beyond cultural barriers, three obstacles to reducing energy are present. One challenge is the identification of buildings which have the greatest need for weatherization upgrades. State of the art energy audits for all buildings would be not only expensive, but impossible to complete. In-building audits cost in the range of US $0.12-0.503/square feet. Given an estimated total floor space (both residential and commercial sectors) in the United States of 352 billion square feet, the total-building audit cost would be in the range of US $42-

175 billion. Arguably this cost might be reasonable, but a second challenge is present. Even if we committed to doing these audits, the number of energy auditors needed to conduct total-building audits at national scale is not enough. In the United States, there are only

14,000 certified home energy auditing professionals according to the Building

Performance Institute which is too few to audit the nearly 140 million houses in the U.S. residential building stock [5]. There is certainly a similar dearth of qualified energy auditors for the 4.9 million commercial buildings in the US [6].

Finally, accuracy in estimating savings can be a real liability to gaining confidence from those interested in making investments in energy saving upgrades. Most problematic is that energy use and savings have been generally over-predicted. For example, a validation study performed at the Energy Center of Wisconsin compared predicted results from Home Energy Rating System (HERS) software to space heating bills for 147

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in Wisconsin. The homes ranged from 1930s pre-retrofit to 1990s ® qualified. The study clearly indicated that the simulation software over-predicted space heating energy use in homes with higher space heating bills, likely older, poorly insulated, leaky homes. The Energy Trust of Oregon performed a similar study to evaluate building energy simulation programs. Three programs were compared: SIMPLE, REM/Rate, and

Home Energy Saver (HES) (EAI/CSG 2009). Detailed audits were conducted and utility bills were collected for 190 homes. The homes were simulated with the three simulation tools, including two levels of detail for HES. The models over-predicted gas use for space heating by an average of 41% in homes built before 1960 and 13% for homes built after

1989 [7]. Lastly, studies in 2012 and 2015 also showed that most energy models do a poor job of predicting actual energy use, especially in older homes. These studies found that energy savings from specific measures are overestimated on average by about 60% for less efficient houses and 17 % for newer houses [8] [9].

Prior to the Trump administration’s election, the US Department of Energy had established as a goal by 2030 is to reduce energy use for both residential and commercial energy sectors by at least 35% [10]. Unfortunately, as has been evidenced by the current administration’s lack of commitment and even contempt for EE and

(RE), the resolve to achieve these previously stated goals has been diminished. Therefore, efforts to limit the effects of climate change need to work within the economic system at least until damage and disruption cultural responses can be established. A number of previous studies have considered economically viable community-scale pathways for energy reduction. Brecha et al. (2011) in analyzing energy consumption data for single- family homes in a Midwestern town in the U.S. were able to create a simple model that

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allows for estimation of energy and saving for heating and cooling for each individual residence considered in their study. They examined of home energy reduction measures/actions, including improved energy behaviors, air reduction, increased insulation, and improved heating and cooling system efficiency improvement.

Their results showed savings from behavioral improvements, insulation plus infiltration reduction, and heavy retrofits (upgrade in all actions/measures considered) to be respectively 13%, 20%, 28%, 33%, and 74% respectively [11].

In similar studies, Hallinan et al. and Villoria-Siegert et al. posed what they termed a ‘worst-to-first’ approach to EE; using available residential building data and historical energy data for all residences in a U.S. Midwestern community to identify the houses yielding the greatest savings from specific measures. Their research showed that this strategy yielding an energy reduction a 32-40% reduction in heating and energy could be achieved at a community-wide cost of $10/mmBTU. However, their analyses employed some questionable assumptions. For example, they assumed that a high weather normalized heating energy for any residence is associated with an equally poor attic and wall insulation, window, and energy effectiveness. The reality is that the energy effectiveness of these components may not be uniform. The absence of attic insulation for example can almost solely be responsible for a high heating for a high energy consumption residence [12] [13].

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CHAPTER 2

PREDICTING RESIDENTIAL HEATING ENERGY CONSUMPTION AND

SAVINGS FROM KNOWN ENERGY CHARACTERISTICS AND HISTORICAL

ENERGY CONSUMPTION

2.1 Abstract

In this chapter, we look at an expanded set of building characteristic data to predict both heating energy consumption for individual residences, as well as savings from the adoption of individual measures – based upon actual building data – not on energy models. Key to this study will be the use of a large number of buildings / residences for which all energy characteristics are known and for which there is reasonable variation in input parameters. The specific case considered addresses hundreds of university-owned student residences in the U.S. Midwest. The housing stock includes houses generally constructed in the early 1900s. Energy saving upgrades have been adopted on many of these houses, but not in a coherent way; thus, this housing set offers a diverse set of energy characteristics. In this study, these energy characteristics have been documented for a total of 139 houses. Historical energy consumption (gas and electric) data for each residence has also been collected. A -learning (artificial neural network) approach is used to develop a single model that accurately predicts heating energy for all houses given the specified energy characteristics. The model prediction of the annual heating energy for all houses considered yields an R2 value of 0.989 and a mean absolute percentage error of

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2.5%. Additionally, the resulting neural net is used to predict savings associated with a small subset of houses in the study which have already been upgraded from a variety of measures. The results show that a collective savings of (Rwall, Rattic,

Rwindow) upgrades, building envelope plus furnace upgrades and building envelope upgrades with furnace and water heater upgrades 25.62%, 26.82%, 27.73% respectively could be obtained. A nearest neighbor approach was used to assess the accuracy of the energy savings estimates for individual house upgrades, by finding the closest house (e.g., the house with energy characteristics nearest to the upgraded energy characteristics of the specific house being considered). The results show that the predicted savings match the actual savings within 2.5 percent for most of the measures considered. Further, they show the potential for establishing larger public databases of building energy characteristics in order to strategically implement energy reduction strategies with the greatest energy savings per cost to implement.

2.2 Background

Actual building information along with actual energy data might be used to estimate savings for any residence. A number of researchers have attempted to predict energy and energy savings using only available building data. For example, Ekici and Aksoy considered three types of building samples with different building form factors

(the ratio between the length and width of the house), that included the building’s transparency ratio (%), orientation relative to the south and wall insulation thickness. They trained a back propagation neural network (NN) to predict energy consumption, and then used the trained neural network results to compare with the numerical results of a conventional calculation method Their comparison showed that the ANN predicted energy

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consumption to an accuracy of 94.8–98.5% [14]. Similarly, Aydinalp et al. utilized a NN methodology to model the appliances, , and space-cooling components of end-use energy consumption in the Canadian residential sector. The inputs for their model were collected from the 1993 Survey of Household Energy Use (SHEU) database and included data on ownership, and information about appliances (refrigerator, range, oven), heating ventilation air-conditioning systems, domestic hot water equipment and weather data

(heating and cooling degree-day data). A NN was used to predict the appliance, lighting and space cooling (ALC) energy consumption in individual residences. Their model achieved a high prediction performance (R2=0.909) and also was able to estimate the electricity consumption of the furnace, fans/ pumps using natural gas or oil, and propane-heated households, as well as other appliances [15]. Koksal and Ugursal developed a conditional demand analysis model to predict residential energy consumption at the national level. They compared their model with those of a neural network and an engineering based model that had been developed earlier. The date was obtained from 1993

SHEU database of Statistics Canada, thus, relatively gross building data was used. The results showed that all three models are were capable of predicting the residential energy consumption. Their engineering model has the highest level of flexibility in evaluating the impact of energy saving measures, however this type of model was noted to not be effective for accounting for socio-economic factors [16].

More typically neural networks have been used to predict energy consumption based upon strictly environmental inputs. For example, Karatasou et al. used feed forward neural networks to model and predict hourly building loads given inputs that included ambient , solar , relative and wind speed. The analysis

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indicated that some of the environmental variables such as the ambient temperature and the solar radiation are important, while others, such as the wind velocity or humidity, can be omitted [17]. Likewise Chonan et al. applied a Bayesian neural network to estimate building energy consumption. consumption and hot water consumption were the targets in their analysis, and the inputs were the standard environmental parameters. Their results showed that the model was able to estimate the energy consumption with a CV-RMSE (Coefficient of Variation of the Root Mean Squared Error) of 19.71% [18]. Aqlan et al. combined artificial neural networks (ANN) with cluster analysis to assess and forecast the energy efficiency of residential buildings. The inputs for their network were the external dry-bulb temperature, relative humidity, global solar radiation, and diffuse solar radiation and the output was the total energy consumption.

Their results showed that the model able to predict the heating and cooling requirements of residential buildings to within 96.9% [19]. Xiang Zhao and Magoules summarized developments in utility analysis and a variety of statistical and machine learning approaches. In their study, they found that each model has its advantages and disadvantage, therefore it is difficult to say which one is better. However, they did conclude that an artificial neural network model has been demonstrated to yield effective prediction of building energy consumption [20].

Some researchers have attempted to include as inputs characteristics of the equipment or buildings being modeled. For example, Yang et al. predicted demand utilizing real time weather data and also the temperature of water leaving the chiller and prior chiller electric demand [21]. Jang et al. utilized interval data for sub-circuits such as lighting and motors to predict overall demand [22]. Neto and Fiorelli added building

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characteristics to predict energy consumption developed from Energy Plus models [23].

Catalina et al. presented a mathematical prediction model to predict heating energy demand, based on the main factors, such as the building’s global heat loss coefficient (G), the south equivalent surface (SES), and the difference between the indoor set point temperature and the sol-air temperature that influences a building’s heat consumption.

Their model presents a very good accuracy (correlation coefficient of 0.987) with an average error of less than 20% [24]. Kreider et al. presented a study that uses NNs trained on input data that included weather data, a timestamp, and energy use data to estimate the thermal resistance (R) and thermal capacitance (C) values of an academic building, after recording the hourly energy consumption for six months [25].

In summary all of the NN energy predictions models have utilized data that were readily available, including weather data, occupancy data, and social demographic data.

Few have attempted to use detailed building characteristics, because such data are not generally available at-scale (i.e., for an entire region). This research seeks to expand the

NN building energy modeling by including known energy characteristics of buildings.

While these data are not readily available, it is possible that they could be, through residential real estate inspections for example. This study then seeks to utilize such data to predict past energy performance and then estimate energy savings based upon actual building data; not physics-based energy models, which as noted in Chapter 1, have tended to over-predict energy savings.

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2.3 Methodology

2.3.1 Building Data Set

An enabler for this research is a large set of houses (over 500) with known physical geometrical and energy characteristics, as well as historical monthly energy data. These residences are owned by the University of Dayton, and thus there is access to all physical characteristics and energy data for each house. This housing stock offers significant diversity in size (ranging from a floor area, Afloor, of 66 to 260 square meters), age (from the early 1900s to new construction) and energy effectiveness. This diversity in energy characteristics and performance has been made possible as a result of staggered improvements made to residences over the past 15 years.

For the purpose of this study, residential building geometrical and energy characteristics were documented for a subset of the houses in the summer of 2015. Energy and building data audits were completed on a total of 139 residential homes that were vacant. The audit data included: a determination of the amount and type of insulation in the walls and attic, areas and types of windows, floor heights, maximum occupancy, appliance (refrigerator, range, oven) specifications, heating ventilation air-conditioning system efficiency, domestic hot water efficiency, the presence of a basement, and the presence of an open basement vent; significant because basement access is restricted from students. Additionally, features such as interior house to attic penetration area, furnace pilot light status, and water heater, refrigerator, and air conditioning “on” status during the unoccupied summer months were documented. Finally, the local county database was used to get detailed features of each residence, including the year built, square footage of each floor, and total square footage. Finally, historical monthly energy

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consumption (gas and electric) data for the period from January 2014 through August 2015 was collected for each residence.

Some general energy characteristic trends were observed. The oldest homes, constructed in the early 1900s, had very little insulation in the walls and . The windows in these homes were generally single-paned. Recently a number of these houses have been renovated or demolished and replaced with new houses. The renovations included double-paned window replacements and the addition of 1.27 cm (1/2-inch) thick insulating wall board to the exterior wall of the residences beneath new siding. The newest homes were constructed to meet the requirements of Energy Star criteria or ICC700

National Green Building Standard. Furthermore, some very old and water heaters have been replaced with higher efficiency units.

The maximum and the minimum values of building characteristics are shown in

Table 1. For example, the measured wall thermal resistance, Rwall, ranges between 0.7 to

2 2 2.43 (m *K/W), Rattic is ranges between 1.14 to 7.1 (m *K/W), and the furnace efficiency is between 60% to 95%.

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Table 1. Residential building geometrical and energy data and range of values made during the summer 2015 audit of 139 houses House characteristics Minimum Maximum 2 Afloor (m ) 66 258 2 Awall (m ) 54 302 2 Awindow (m ) 7 27 2 Aattic (m ) 43 245 Attic penetration area (cm2) None 3716 Basement vent area (cm2) None 348 2* Rattic (m K/W) 1.14 7.1 2* Rwindow (m K/W) 0.18 0.35 2* Rwall (m K/W) 0.70 2.43 2* Rbasement (m K/W) 0.70 0.88 Number of occupants 2 12 Furnace efficiency (%) 60% 95% Energy factor for water heater 0.55 0.96 SEER (AC) None 16

As the goal of this work is to predict energy consumption and then ultimately energy savings from specific energy efficiency measures based upon actual building and energy data, it is essential to document that the building set has reasonable variation in characteristics that influence energy consumption. Thus, Figure 1 illustrates the nature of the variance in the measured building geometrical characteristics. Variation is essential for developing a single relationship between these characteristics and the resulting energy consumption for each residence. It is obvious that there is significant variation in floor, wall, and window area.

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35 35 30 30 25 25 20 20 15 15 10 10

Number of of housesNumber 5 Number of of Number houses 5 0

0

70 80 90

110 120 130 140 150 160 170 180 190 200 210 220 100 2

More A (m ) 2 wall Afloor (m )

(a) (b)

70 35 60 30 50 25 40 20 15 30 10 20 5

10 of housesNumber 0

Number of of housesNumber

60 70 80 90

0 50

100 110 120 130 140 150 160

10 15 20 25 30 More 2 A (m2) Awindow (m ) attic

(c) (d)

Figure 1. The nature of the variance in the measured building geometrical characteristics:

(a). Afloor; (b). Awall; (c). Awindow; and (d). Aattic

Figure 2 illustrates histograms for the various energy characteristics, including:

Rattic; Rwindow; Rwall; AC SEER; water heater energy factor; furnace efficiency; refrigerator energy factor; and interior to attic penetration area. It is obvious that there is significant variation in all characteristics except the thermal resistance for windows (Figure 2b), although these energy characteristics are not generally uniformly distributed, which would ideally be the case.

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60 160 50 140 120 40 100 30 80 20 60

10 of Number houses 40 Number of of housesNumber 20 0 2 3 4 5 6 7 8 0 R (m2*K/W) 0.2 0.3 0.4 attic R (m2*K/W) window

(a) (b)

80 70 60 60 50 40 40 30 20 20

Number of of houses Number 10 Number of of housesNumber 0 0 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2 Rwall (m *K/W) Furnace efficiency (%)

(b) (d)

90 60 80 70 50 60 40 50 40 30 30 20

Number of of housesNumber 20 Number of of housesNumber 10 10 0 0 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 None 8 10 12 14 16 Water heater energy factor SEER(AC)

(e) (f)

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60 120 50 100 40 80 30 60

20 40

Number of of Number houses Number of houses of Number 10 20

0 0

0.54 0.26 0.30 0.34 0.38 0.42 0.46 0.50 0.58 0.62 0.66 0.70 0.72

Refrigerator energy factor (m3-day/kWh) Attic Penetration area(cm2)

(g) (h)

Figure 2. Building energy characteristics histograms for: (a). Rattic; (b). Rwindow; (c). Rwall; (d). furnace efficiency (%); (e). water heater energy factor; (f). SEER (AC); (g). refrigerator energy factor; and (h). attic penetration area.

2.4 Identification of Characteristics Having Greatest Impact on Energy

Consumption Using a Random Forest Approach

A Random Forest (RF) approach is used to identify the building geometry and energy characteristics (predictor variables) having the greatest effect on the heating energy consumption; in order to select the factors should be included in the data-based model described in the next section. This technique was originally developed by Breiman (2001) for both classification and regression problems [26].

The initial input parameters to be considered are those shown in Table 1. In addition, since heating and cooling loads depend fairly linearly on the ratio of area to thermal resistance for walls, attic, windows, and basement, the random forest algorithm considered as inputs Ai/Ri, where Ai and Ri are respectively the envelope component area and thermal resistance. Additionally, while tests could have been performed

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on each of the audited houses to gauge infiltration rates, a decision was made to not utilize such data since gathering infiltration data on houses at scale would be costly and unmanageable. Furthermore, the energy consumption for each residence (natural gas) was included as a predictor for each residence. Thus, three other characteristics were considered as possible predictors: namely the heating slope, HSgas (W/K), the heating temperature balance point temperature, Tbalh,gas and the weather-independent electric intensity,

2 퐸푏푎푠푒푙푖푛푒,푒푙푒푐푡푟푖푐, (kWh/m ). The gas parameters were calculated from a 3-parameter regression approach (Prism method) to suit the monthly gas consumption (MJ/month) to the average outdoor temperature over the scale period of the model:

퐸푖,푔푎푠 = 퐸푏푎푠푒푙푖푛푒,푔푎푠,푖 + 퐻푆푔푎푠(푇푏푎푙ℎ,푔푎푠 − 푇표푢푡푠푖푑푒) (2.1)

The electricity parameter, 퐸푏푎푠푒푙푖푛푒,푒푙푒푐푡푟푖푐 was obtained from 3-parameter regression [27] of the form:

퐸푖,푒푙푒푐푡푟푖푐 = 퐸푏푎푠푒푙푖푛푒,푒푙푒푐푡푟푖푐,푖 + 퐶푆푒푙푒푐(푇표푢푠푖푑푒 − 푇푏푎푙푐) (2.2)

Assumed here is that the weather-independent electric consumption results in heat addition to a facility, and thus has some impact on natural gas consumption

The final set of predictors for energy consumption used for the RF are presented in

Table 2. In this table the order of importance characteristics in predicting energy consumption is noted for each predictor. Figure 3 shows characteristics that have high influence on energy consumption. The measure of how each variable contributes to the homogeneity of the nodes and leaves in the resulting random forest is called the mean decrease in Gini coefficient. Variables that have a higher decrease in Gini coefficient, split nodes into nodes with higher purity. Variables that split nodes into nodes with higher purity

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have a higher decrease in Gini coefficient [28]. It is apparent from this Figure that HSgas has the highest influence on energy consumption, while Rbasment, attic penetration area, basement duct vent open, and basement duct vent area have significantly less correlation to energy consumption. As a consequence, these variables were not considered in the neural net model described in the next section.

Table 2. Predictors for gas consumption ranked by influence on energy consumption Order of variable importance (1- Predictors most important) 1 Heating slope, HSgas (W/K) 2 Awall /Rwall 3 Baseline electric intensity (Baselinee) (kWh/m2) o 4 Tbalh,gas ( C) 5 Furnace efficiency (ηfur) (%) 6 Aattic / Rattic 2 7 Afloor (m ) 8 Awindow / Rwindow 9 Energy factor for water heater (EF,WH) 10 Number of occupants 11 Attic penetration area (cm2) 12 Basement duct vent area (cm2) 13 Basement duct vent open 14 Rbasement

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Figure 3. Characteristics that have high influence on energy consumption

2.5 Artificial Neural Network Model for Predicting Heating Energy

Artificial neural networks have been widely used in many applications of energy modeling. The main advantage of artificial neural networks is their ability to model non- linear processes, such as utility loads [29]. In this effort a feedforward ANN strategy was used to associate energy use with the residential building geometry and energy characteristics determined to be the most important factors in the previous section. The goal was to develop one ANN model that would predict monthly and annual natural gas energy consumption for each and every house; not a unique ANN model for each house.

Table 3 lists the inputs discerned to be most important in predicting energy consumption based upon the random forest model. It also identifies the output or target value. Overall, the dataset consists of 11 data features that will be used as inputs.

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Table 3. The inputs data used in ANN model constriction Variable Input Output 2 Afloor (m ) X

Aattic /Rattic (W/ K) X

Awindow /Rwindow (W/ K) X

Awall /Rwall (W/ K) X Furnace efficiency (%) X Energy factor for water heater X Baseline electric intensity (kWh/m2) X Number of occupants X Heating slope, HSgas (W/K) X Heating balance point temperature (oC) X Average monthly outdoor temperature (oC) X Monthly natural gas usage (MJ/month) X

Table 4 illustrates sample input of the data. Data for two houses are included in this table. For each residence, there are N inputs associated with each month of recorded energy data, with all inputs for each residence the same, except for the monthly average temperature during the meter period. The building characteristics, utility analysis, energy consumption, and monthly average outdoor temperature were divided into two periods, the heating and cooling seasons, for which NNs were developed for heating season. This period was October through April. The dataset consist of 973 samples.

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Table 4. Sample input data used in ANN model constriction Input Factors Samples House 1 House 2 Oct Nov ..… Apr Oct Nov .. Apr 2 Afloor (m ) 104 104 ..… 104 124 124 .. 124 Aattic /Rattic (W/K) 23.5 23.5 ..… 23.5 40.74 40.74 .. 40.74 Awindow /Rwindow(W/K) 53.3 53.3 … 53.3 50.4 50.4 .. 50.4 Awall /Rwall(W/K) 194 194 … 194 175.4 175.4 .. 175.4 Furnace efficiency (%) 95 % 95 % …. 95 % 70 % 70 % .. 70 % Energy factor for water 0.55 0.55 …. 0.55 0.63 0.63 .. 0.63 heater Baseline electric 66.74 66.7 …. 66.74 60.82 60.82 .. 60.82 intensity (kWh/m2) Number of occupants 6 6 ….. 6 5 5 .. 5 Heating slope, HSgas 3.80 3.80 ..… 3.80 4.10 4.10 .. 4.10 (W/K) Heating balance point 14.72 14.7 ..… 14.72 19.63 19.63 .. 19.63 temperature (oC) 2 Average monthly 10.6 4.97 ….. 10.96 10.96 4.97 .. 10.96 outdoor temperature (oC) Output (MJ/months) Monthly natural gas 5811 13344 … 16680 5811 1775 . 19901 usage (MJ/month) 7

The ANN model used 70% of all data samples for training, 15% testing, and 15% validation. An effort was made to determine the combination of the number of hidden layer units and the learning algorithm that produces the best prediction performance. Ultimately, it was found that the network trained with the learning algorithm associated with two hidden layers and 11 hidden neurons resulted in the highest R2 (correlation coefficient).

A paired-sample t-test was applied to compare the predicted total gas energy consumption (for an entire winter) with the actual total gas consumption to demonstrate the statistical significance of the predicted data. The null hypothesis for the t-test is that the means from the actual data and predicted data are not equal. The results of the t-tests, shown in Table 5, indicate that there is no significant difference between the two data series paired

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sample tests. This statistical analysis shows that the means for the two datasets are very similar and the probability value, 0.921, indicates that there is no significant difference between the two data sets at the specified significance level of 0.05.

Table 5. The results of the t-tests for actual vs. predicted Actual Predicted

Samples 139 139

Mean 1206 1206

Standard deviation 383 380

Standard error mean 32 32

95% CI for difference: (-85.53, 94.63)

Estimate for difference: 4.54

T-Value 0.1

P-Value 0.921

2.6 Results and Discussion

2.6.1 Predicting Energy Consumption

The trained neural network was first used to predict the historical monthly natural gas consumption for dwellings in the sample. The equation below describes the mean absolute percentage error that was used to evaluate the performance of the developed

ANN model:

1 푛 푦푖 −푥푖 푀퐴푃퐸 = ∑푖=1 | | (2.3) 푛 푥푖

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In the above equation, n is the number of data points, xi is the actual monthly natural gas consumptions, and yi is the predicted monthly natural gas consumption. Figure 4 displays the actual natural gas consumption plotted against the predicted natural gas consumption for the heating season (October through April) for a sample group of 40 houses included in the study. It is clear that this subset of houses includes higher- and lower-energy consumption houses. The R2 value for the training, testing, and validation data by ANN is

0.989 and the mean absolute percentage error is 2.5% for the 139 University of Dayton houses. In comparison, the MAPE and correlation coefficient (R2) using a three-parameter heating regression using the PRISM methodology is 9.2% and 0.88 respectively. The major indicator is the additional building parameters and energy characteristics helped to improve the ability to predict energy consumption.

220000 200000 180000 160000 140000 120000 100000 80000 60000

Natural Gas Consumption (MJ) Consumption Gas Natural 40000 20000 0 1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031323334353637383940 Sample of Houses

Actual Predicted without upgrade

Figure 4. The actual natural gas consumption plotted against the predicted natural gas consumption for a random sampling of houses for the heating season (October through April).

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2.6.2 Estimating Natural Gas Savings from Retrofit Specific Measures

Having established confidence in predicting the heating energy consumption, the trained neural net was used to investigate potential savings from specific upgrades. The existing house geometrical and energy characteristic data was used to create new data with upgrades. Table 6 shows the upgrades that were considered, the R-values for the attic and wall insulation upgrades are, respectively, 7 and 2.3 m2*K/W, upgrades of water-heaters

(to an EF of 0.96), furnaces (to an efficiency of 95%) and windows (to triple-paned).

Table 6. The upgraded data used in ANN model constriction Variable Input 2 Rattic (m ˖K/W) 7 2 Rwindow (m ˖K/W) 0.53 2 Rwall (m ˖K/W) 2.3 Furnace efficiency, 훈퐟퐮퐫 (%) 95% Instant 0.96 Energy factor for water heater Storage 0.70 Heating slope, HSgas (W/K) Calculation

Two other predictors had to be estimated post upgrade were the model developed previously to be employed to predict energy consumption after upgrade; namely the heating slope, HSgas, and the heating temperature balance point temperature, Tbalh, gas.

Both of these likely would change after upgrades. Estimation of the change in these after the upgrade was based upon the definition of the HSgas.

퐴푖 ∑ ( ) 퐻푆 = 푅푖 ⁄ (2.4) 푔푎푠 η푓푢푟

If any of the R-values and/or furnace efficiency are changed, the heating slope would be modified. More specifically for example, for a wall insulation improvement, the HSgas for an upgrade can be expressed in terms of:

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1 1 퐻푆푔푎푠,푢푝푔푟푎푑푒,푤푎푙푙 = 퐻푆푔푎푠,푐푢푟푟푒푛푡 − [( ) − ( )] ∗ 푅푤푎푙푙,푐푢푟푟푒푛푡 푅푤푎푙푙,푢푝𝑔푟푎푑푒

퐴 ( 푤푎푙푙 ) (2.5) ηfur

The temperature balance point temperature, Tbalh,gas, is based upon the heating slope gas, and thus it will change for induvial and completive measures for specific houses.

At warmer ambient air , the residents will feel the heat loss from the house if the heating slope is high. On the other hand, when the heating slope is reduced due to efficiency improvement, the temperature balance point temperature, e.g., the temperature below which the temperature balance point temperature heating is required will decrease.

By definition, the building balance point temperature is associated with a balance between the heat leaving the building with the internal heat gains for example, internal electrical loads, which are assumed to be converted to heat.

퐴푖 푄 = ∑ ( ) ∗ (푇 − 푇 ) (2.6) 푙표푠푠푒푠 푅푖 푖 푏푎푙ℎ,푔푎푠

The summation term can be linked to the heating slope in the upgraded condition according to the following:

퐴푖 ∑ ( ) = 퐻푆 ∗ η (2.7) 푅푖 푔푎푠,푢푝푔푟푎푑푒 fur

The heat loss at the heating temperature balance point temperature is as follows:

푘퐵푡푢 푄 = ∗ 퐸 (2.8) 푙표푠푠푒푠 푘푊ℎ 푏푎푠푒푙푖푛푒,푒푙푒푐푡푟푖푐

Combining equations (2.4), (2.5), (2.6) yields the following equation for the balance point temperature after the upgrade.

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푘퐵푡푢 ∗ 퐸 푘푊ℎ 푏푎푠푒푙푖푛푒,푒푙푒푐푡푟푖푐 푇푏푎푙ℎ,푔푎푠,푢푝푔푟푎푑푒 = 푇푖 − ( ) (2.9) 퐻푆𝑔푎푠,푢푝𝑔푟푎푑푒∗ ηfur

푘퐵푡푢 ∗ 퐸 푘푊ℎ 푏푎푠푒푙푖푛푒,푒푙푒푐푡푟푖푐 푇푏푎푙ℎ,푔푎푠,푐푢푟푟푒푛푡 = 푇푖 − ( ) (2.10) 퐻푆𝑔푎푠,푐푢푟푟푒푛푡∗ ηfur

In these equations, 푇푖 is the building set-point temperature, given in °F. The change in the heating balance point temperature after upgrade can therefore be estimated as:

푘퐵푡푢 ∗ 퐸푏푎푠푒푙푖푛푒,푒푙푒푐푡푟푖푐 1 1 ∆푇 = (푘푊ℎ ) ∗ ( − ( ) (2.11) 푏푎푙ℎ,푔푎푠 η 퐻푆 퐻푆 fur 𝑔푎푠,푐푢푟푟푒푛푡 𝑔푎푠,푢푝𝑔푟푎푑푒 resulting in the following estimate for the new heating balance point temperature.

푘퐵푡푢 ∗퐸푏푎푠푒푙푖푛푒,푒푙푒푐푡푟푖푐 1 푇 = 푇 − ( 푘푊ℎ ) ∗ ( − 푏푎푙ℎ,푔푎푠,푢푝푔푟푎푑푒 푏푎푙ℎ,푔푎푠,푐푢푟푟푒푛푡 η 퐻푆 fur 𝑔푎푠,푐푢푟푟푒푛푡

1 ( ) (2.12) 퐻푆𝑔푎푠,푢푝𝑔푟푎푑푒

The input data for the upgraded houses was fed in to the ANN model to estimate potential energy savings from individual and a collective grouping of measures. The savings from any or all upgrades for specific houses were based upon the following, where

퐸푡표푡푎푙,푐푢푟푟푒푛푡 is the total energy consumption currently and 퐸푡표푡푎푙,푖푚푝푟표푣푒푑 is the total energy consumption for the improved house, as determined from the employed NN with the improved energy characteristics.

푆푎푣푖푛푔푠 = 퐸푡표푡푎푙,푐푢푟푟푒푛푡 − 퐸푡표푡푎푙,푖푚푝푟표푣푒푑 (2.13)

The potential energy savings from implementation of individual measures for a winter season was determined from the difference between predicted energy consumption

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based upon the improved inputs and the actual consumption. The average winter season savings for the entire housing set for wall and attic insulation addition, and upgrades of windows, furnaces, and water heaters are shown in Table 7. Moreover, Figure 5 shows histograms of percentage gas consumption savings across all houses from each of the individual measure during winter season, including additional wall and attic insulation and upgrades of windows, furnaces, and water heaters. These results make it clear that the potential natural gas savings of adding wall insulation is higher than for upgrades of windows and attic insulation addition simply because the current condition of insulation is generally poor. Apparent from Table 7 is that wall insulation has the greatest savings, given the generally poor insulation in the walls. Heating and system upgrades have the least savings, as a majority of existing gas furnaces and water heaters have been upgraded to high efficiency.

Table 7. Average gas consumption and percentage savings from individual retrofit measures

Furnace Energy factor Variable R R R wall attic window efficiency water heater Average gas 27657 MJ 4841 MJ 5381 MJ 3874 MJ 2690 MJ consumption savings Average percentage gas consumption savings (21.3%) (3.7%) (4.14%) (2.95%) (2.08%)

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30 40 35 25 30 20 25 15 20

Number of houses of Number 15 10

Number of houses of Number 10 5 5 0 0

0% 2% 4% 6% 8% 10% 12% 14% 16%

0% 5%

15% 10% 20% 25% 30% 35% 40% 45% 50% 55% 60% Natural gas savings Natural gas savings (a) (b)

35 45 30 40 35 25 30 20 25 15 20

15 Number of houses of Number

Number of houses of Number 10 10 5 5 0 0 0% 2% 4% 6% 8% 10% 12% 14% 0% 2% 4% 6% 8% 10% 12% 14% Natural gas savings Natural gas savings (c) (d)

60 50 40 30 20

10 Number Number houses of 0 0% 2% 4% 6% 8% 10% 12% Natural gas savings

(e)

Figure 5. Histograms of percentage gas consumption savings across all houses from each of the individual measure during winter season for: (a). wall insulation; (b). attic insulation; (c). window upgrades; (d). furnace efficiency upgrades (%); and (e). water heater energy factor upgrades

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Now, the potential energy savings from a collective grouping of retrofit measures including; building envelope (Rwall, Rattic, Rwindow) upgrades, building envelope plus furnace upgrades and building envelope upgrades with furnace and water heater upgrades.

Table 8 shows a comparison between the collective groupings of measures savings. The order of the upgrades that was selected because the generally better cost effectiveness of envelope upgrades, over furnace upgrades, over water heater upgrades.

Table 8. A comparison between the collective groupings of measures savings

Envelope Envelope and Envelope, furnace, and Variable upgrade furnace upgrade water heater upgrade Average gas 33253 MJ 34760 MJ 35513 MJ consumption savings Average percentage gas (25.62%) (26.82%) (27.37%) consumption savings

Upgrading gas furnace and water heaters will not add more savings because the furnaces and water heaters are good from an energy efficiency perspective and the cost of upgrading both systems is high. Histograms of the percentage gas consumption savings from among all houses from all energy efficiency measures during winter season is shown in Figure 6. The model predictions help to inform a more strategic implementation process

– e.g., improving the worst first.

30 25 25 20 20 15 15

10 10 Number of house of Number

5 5 Number of housing of Number

0 0

0% 5%

10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% Natural gas savings Natural gas savings

(a) (b)

28

30

25

20

15

10 Number of houses of Number 5

0 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60%

Natural gas savings

(c) Figure 6. Histograms of percentage gas consumption savings from among all houses from all energy efficiency measures during winter season for: (a). envelope upgrade; (b). envelope and furnace upgrade; and (c). envelope, furnace, and water heater upgrade

2.7 Validating Savings Using a K-Nearest Neighbor Approach

A wide variations in energy characteristics of the houses are still observed between improved and unimproved houses during the past years. But, there is no pre- and post- improvement data, as no database has been documented for both the improvements that have been made for each house and when they were made. Validating energy saving is important, so a “nearest neighbor” method was used [30]. In this approach, we first identify the house that will be improved from among the group of houses and compare it in its improved state (energy characteristics improved) to the collective grouping of houses in the study. That house with geometrical, energy characteristics, and estimated parameters

(heating slope and balance point) closest to the considered upgraded house is called the nearest neighbor. If the nearest neighbor after improvement is deemed to well represent the post-improvement state of the considered house, then the actual energy consumption of the nearest neighbor will be like the energy consumption of the improved house. Therefore,

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the difference between the existing energy consumption of the unimproved house and the energy consumption of the nearest neighbor house after upgraded would be the actual energy savings.

In this section, six test houses were tested to predict energy saving by using K- nearest neighbor method. To determine the ‘closeness’ of upgraded homes to an existing homes, a Euclidean squared distance metric was used. The Euclidean squared distance metric between two data set includes computing the sum of the square difference between the upgraded energy characteristics of the test home and the characteristics of the nearest neighbor home. This error is defined as:

푛 2 퐸푟푟표푟 = ∑푗 (푓푢,푗 − 푓푐,푗) (2.14)

where n is the number of inputs, fu, j are the upgraded energy characteristics, and fc, j are the current energy characteristics.

Figure 7 shows a comparison between the results from ANN model with K-nearest neighbor approach to predict the energy usage for six houses include higher and lower energy consumption. The ANN approach to predict savings compares very closely to the energy consumption of the nearest neighbor residence. The results from both methods are very close and the mean absolute percentage difference is 5%. Noticeable is that if the

Euclidean error associated with the best nearest neighbor is relatively small, the difference between the neural net estimation of savings and the nearest neighbor approach is smaller.

Given that the K-nearest neighbor approach was based upon actual data, it can be concluded that the ANN-based prediction of savings is very good.

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200000 error = 0.15 180000

160000 error = 0.025 error = 0.066 140000 error = 0.055 120000

100000 error = 0.007 80000 error = 0.0001 60000

Natural gas consumption Natural consumption gas (MJ) 40000 20000 0 1 2 3 4 5 6 Sample of houses Predicted after upgrade (ANN) Current houses (Nearest neighbor)

Figure 7. Comparison of predicted natural gas usage by ANN model with nearest neighbour method during winter season

2.8 Conclusions

A machine-learning (artificial neural network) approach is shown to be a useful tool for predicting energy savings from retrofit projects. The ANN model developed in the present study is based on a back-propagation algorithm. Hundreds of student residences owned by the University of Dayton were used in this study. The inputs of the ANN model for training and testing are considered as Afloor, Awall /Rwall, and Aattic /Rattic, Awindow /Rwindow, the heating temperature balance point temperature, Tbalh,gas, the heating slope, HSgas, baseline electric intensity, and the monthly average outdoor temperature. An ANN model was developed. The coefficient of correlation of the ANN model between the ANN model output and real data is 0.989 and the mean absolute percentage error is 2.5%. The results also show the possibility of accurately utilizing the developed neural net to predict savings for specific energy efficiency upgrades, with predicted consumption for a small set of

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houses matching to within 5% of the actual consumption in houses identified as nearly identical to the improved house.

The results show that accuracy in estimating savings by using ANN model can be a real asset for attracting confident investment in the savings and can be used to model accurately the energy consumptions in the residential sector. The ability to make data available for machine learning models are not generally possible, there is real potential to inform documentation of building energy characteristics in residential inspections; and potential to utilize the established information created to develop strategic energy reduction efforts by utilities.

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CHAPTER 3

DATA-BASED APPROACH FOR MOST COST EFFECTIVE RESIDENTIAL

ENERGY REDUCTION

3.1 Abstract

Many U.S. utilities incentivize residential energy reduction through rebates, often in response to state mandates for energy reduction or from a desire to reduce demand to mitigate the need to grow generating assets or simply from a desire to provide service to customers. The assumption built into incentive programs is that the least efficient of residences will more likely take advantage of the rebates. This isn’t however always the case. The objective of this study is to show the potential for prioritized incentivization, e.g., incentivization that delivers the greatest energy savings per investment through an entire community. A data mining approach leveraging known building and energy characteristics is used to develop a single model to accurately predict energy consumption for a grouping of houses, which collectively can be considered to be representative of all residences within an entire community. From this model, natural gas consumption and cost savings and corresponding implementation costs associated with adoption of the most impactful energy reduction measures for each residence can be estimated. From these savings and cost estimates a sequential energy reduction strategy can be developed, whereby the most cost effective measures from within the entire utility district are addressed first and so on. From such a strategy, the results show that an energy reduction of 36% can be achieved at a

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levelized cost of less than $14/mmBTU, thus demonstrating the strong potential of this approach. A corresponding Economic Input-Output Analysis is used to capture the cascading community economic impacts emerging from this strategy. Energy efficiency measures such as installation of wall and attic insulation, as well as window, furnace, and water heater upgrades are considered. The results show that for the roughly 45,000 single family residences in the City of Dayton, an initial energy efficiency investment of $26.1M to achieve this magnitude of savings could result in a total cascading multiplier economic impact of $41.2M. In addition, the dollar amount saved from energy efficiency investments will result in additional economic impacts stemming from the annual energy savings of $2.21M for the lifetime of the considered EE measures. Thus, this ‘worst-to- first’ strategy offers a very promising community economic development initiative.

3.2 Background

Gillingham et al. (2006) evaluated several EE policies that aimed at increasing EE through improved appliance standards, financial incentive programs, information and voluntary programs, and management of government energy use with varying degrees of consistency in the implementation and achievement. They found that appliance standards and utility-based demand-side management (DSM) could yield energy savings of up to 4 quads/year and carbon emissions reductions of up to 63 million metric tons/year (~4% of emissions in 2000) [31]. Koomey et al. (1998) utilized building sector information from many sources to create policy scenarios for increasing EE. They estimated that an aggressive EE policy could yield energy savings and carbon reduction emissions of respectively 20% and 40% by 2020 [32].

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EE isn’t just about the economic impact from savings; it is also associated with economic benefit emerging from job creation and indirect benefits emerging from more available income in a community.

Choi et al. (2015) estimated the community wide economic and environmental energy efficiency investment on local residential sector by developing a systematic framework. They implemented various EE measures in Montgomery County, Ohio by employing Economic Input-Output Analysis to estimate direct, indirect, and induced economic impacts. Their results show that a $14 million investment in heating, ventilation and air conditioning (HVAC) systems for residences in the community could yield a $22 million total economic impact [33].

Improving the cost effectiveness of utility energy reduction programs by targeting rebate dollars to residences yielding the greatest savings was the central driver to some relatively recent research. Sadineni et al. (2011) estimated the cost benefits from residential energy retrofits in the Desert Southwest region of the US. They developed individual residential energy models to predict the annual energy savings and payback periods for various measures. The aim of their study to develop an economically driven home energy rebate program that can help the consumers/home-owners to lower their electricity bills

[34].

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3.3 Developing a Single ANN Model for All Residences to Predict Heating

Consumption and Savings from Individual Energy Efficiency Measures

The previous study mentioned in Chapter 2 described a process for establishing an artificial neural net model to predict monthly natural gas energy use based upon known residential building characteristics and historical natural gas consumption. The model predictors are reiterated in Table 9.

Table 9. Predictors used in ANN model to predict energy consumption in all houses

Variable Input Output 2 Afloor (ft ) X o Aattic /Rattic (Btu/hr- F) X o Awindow /Rwindow (Btu/hr- F) X o Awall /Rwall (Btu/hr- F) X Furnace efficiency (%) X Energy factor for water heater X Baseline electric intensity (kWh/ft2) X Number of occupants X o Heating slope, HSgas (Btu/hr- F) X Heating balance point temperature (oF) X Average monthly outdoor temperature (oF) X Monthly natural gas usage (ccf/month) X

The model developed in Chapter 2 was demonstrated to predict gas energy consumption with a mean absolute percentage error of 2.5% and a coefficient of correlation is 0.989.

Figure 8 documents the accuracy of the model in predicting annual natural gas energy consumption (ccf) for a sampling of houses. In this Figure, the actual (blue) and predicted

(red) consumption for each house are paired together.

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2200 2000 1800 1600 1400 1200 1000 800 600

400 Natural GasConsumption (ccf) 200 0 5 10 15 20 25 30 35 40 Sample of Houses Actual Predicted without upgrade

Figure 8. The actual natural gas consumption plotted against the predicted natural gas consumption for a random sampling of houses for the heating season (October through April).

With a single model predicting natural gas energy consumption developed for the most important energy characteristics for single-home residences, potential energy savings from individual measures could be both individually and collectively determined. With implementation costs assigned to the individual measures, the cost effectiveness of each possible EE measure among an entire grouping of houses could be assessed. A hierarchy of potential investments in terms of cost effectiveness can be established from this assessment.

The following extends this previous work to assess more completely the economics of this data-driven ‘worst-to-first’ strategy for implementation of EE measures and to evaluate the holistic community-wide economic impact from this approach.

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3.4 Economic Analysis of Sequential Adoption of Most Cost Effective EE Measures

3.4.1 Prioritized Energy Savings Among the Aggregate Set of Residences

Given an ability to accurately estimate energy cost savings from various EE measures for all residences within a collective grouping of houses (ideally across an entire utility district), a prioritized strategy for investment can be developed. A levelized cost savings ($/mmBTU) metric is used for this prioritization, similar to that employed by

McKinsey [11] [13][34]. To estimate the levelized cost of investments, the lifetime of each EE measure must first be specified. Here, a lifetime of 30 years is assumed for all weatherization improvements and 20 years for the furnace and water heater upgrades

[35]. In addition, an initial fuel cost of 0.6 $US/therm was assumed, along with an annual energy cost escalation of 3%. Additionally, when assessing economic viability based on the time value of money, the minimum attractive rate of return (MARR) was chosen for the project in order for it to be a financially acceptable rate of return. The minimum attractive rate of return was assumed to be 7%.

The levelized cost of savings is calculated as follows. First, the initial cost of an investment is calculated, as shown in Equation (3.1).

Initial cost = Units × Cost ($/unit) (3.1)

Next, given savings for each measure estimated as described in Section 2, the fuel cost savings associated with an individual EE measure is calculated in the first year after implementation as shown in Equation (3.2).

Fuel cost savings = Initial fuel cost × 퐴nnual natural gas savings (mmBTU) (3.2)

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The net present value (NPV) associated with the fuel cost savings over the investment lifetime is determined as:

푁 푆 푁푃푉 = −퐶 + ∑ 푦푒푎푟푠 푡 (3.3) 푡=1 (1+푟)t

Where C is the capital cost of the investment, St is the monetary fuel savings at time step t

(assumed to be constant over the project lifetime), r is the real discount rate (assumed to be 7%), and Nyears is the lifetime of the project. The levelized cost of fuel savings ($/ mmBTU) is then calculated as:

푁푃푉 Levelized cost of fuel savings = (3.4) Annual natural gas savings (mmBTU)×푁푦푒푟푎푠

In order to calculate the levelized cost of fuel savings from Equation (3.4), the initial costs associated with implementation of each of the EE measures must be estimated.

Table 10 shows the assumed cost per square feet for weatherization improvements, as well as upgrade costs for heating and water heating equipment to highest efficiency systems

[13]. Those zones utilized for costing each measure are those applicable to the improvement. For example, attic insulation only depends upon the attic area. The furnace heating is dependent upon all of the floor area, etc.…

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Table 10. Cost per square feet for weatherization improvements, and heating equipment replacement Measure Unit Cost Add insulation to wall $0.73/sf Add insulation to attic $0.7/sf Upgrade window $21/sf Upgrade heating equipment $1.76/sf Upgrade water heating equipment $1.76/sf

Using Equations (3.1) – (3.4) and the energy savings estimates for all EE measures for the study set of houses developed via the process described in Chapter 2, the levelized costs for all EE measures on a house by house basis are determined. Figure 9 shows the results were every house in this dataset to be upgraded to highest efficiency standards

(blue). In this Figure, a comparison is made to the similar estimates made by McKinsey, which represents a ‘break-even’ investment scenario equivalent to the cost of natural gas predicted in 2020 in the McKinsey study (orange). McKinsey estimated a levelized cost for wall, attic insulation addition, window replacement, furnace upgrades, and water heater upgrades to be $13.3, $6.7, $8.5, $12.6, and $22 /mmBTU respectively [36]. With the exception of wall insulation upgrades, the cost comparison to the McKinsey estimates are not favorable. It is clear that blind investment in any EE measure across the entire spectrum of houses doesn’t offer even close to attractive cost effectiveness.

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$52

358 $22

Water heater upgrade $34

456 $12.60 upgrade Furnace Average levelized cost $33 savings ($/mmBtu) for each

712 $8.50 measure

upgrade Window $13 McKinsey cost for each

642 $6.70 measure

Attic

addition insulation

Annual Annual energy savings (mmBTU) $7

3668 $13.30

Wall

addition insulation $0 $10 $20 $30 $40 $50 $60

Figure 9. The annual energy savings in million Btu for various measures vs. average levelized cost savings ($/ mmBTU) for all residences

3.4.2 Levelized Cost of Prioritized Investment in Energy Efficiency Measures Among the

Collection of Houses

Since the objective of this work is to establish the priority energy reduction investments, histograms of the levelized cost of investments for each measure considered are presented in Figure 10. These histograms show the variance in levelized cost of the energy savings ($/mmBTU) among all houses for all energy efficiency measures. Figure

10 illustrates that there is potential savings in energy consumption among all the measures.

It is clear that that wall upgrades have the lowest levelized cost among all potential savings measures.

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120 50 45 100 40 80 35 30 60 25 20 40 Number of houses of Number 15

Number of houses of Number 20 10 5 0 0 10 20 30 40 50 60 70 80 90 100 Levelized cost of the energy savings Levelized cost of the energy savings ($/mmBTU) wall insulation ($/mmBTU) attic insulation

(a) (b)

60 30 50 25 40 20 30 15 20 10

10 houses of Number 5 Number of houses of Number 0

0

20 40 60 80

140 100 120 160 180 200 220 240

80 10 20 30 40 50 60 70 90

More

110 120 130 140

Levelized cost of the energy savings 100 More ($/mmBTU) window upgrade Levelized cost of the energy savings ($/mmBTU) furnace upgrade

(c) (d)

30 25 20 15 10 Number of houses of Number 5 0 20 40 60 80 100 120 140 160 180 200 220 240 More

Levelized cost of energy savings ($/mmBTU) water heater upgrade

(e)

Figure 10. Histograms of energy savings measures for the levelized cost of energy savings ($/mmBTU) across all houses for: (a). wall insulation; (b). attic insulation; (c). window upgrades; (d). furnace upgrades; and (e). water heater upgrades

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Figure 11 shows the levelized cost of the savings from each measure with measure implementation done sequentially among the whole housing set in order of lowest levelized cost. It also shows a line representing the fuel cost predicted in 2020 ($14/mmBTU). Thus, for each measure, among the collection of houses, the measure with the lowest levelized cost is implemented first; the next lowest second; and so on. As in Figure 9, the McKinsey break-even cost per energy savings is shown, thus revealing that there is potential value in prioritizing investment. The lowest levelized costs for each measure all fall below the

McKinsey benchmark. For example, levelized costs that fall below the benchmark form water heater and heater upgrade can be realized for less than 5% of the residences. In contrast, 78% of the houses considered in this study offer an economically viable investment in wall insulation.

120 McKinsey benchmark Attic insulation Window upgrade 100 Furnace upgrade Water heater upgrade Wall insulation 80

60

40

20 Levelized cost savings ($/mmBTU) savings cost Levelized

0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of houses

Figure 11. Levelized cost of fuel savings for houses presented in ascending order of cost effectiveness for the houses during winter season

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Given these histograms, a strategy for adoption of EE that preferences residences having the lowest levelized cost per savings makes sense. Were this strategy to be implemented for each of the considered EE measures in ascending order of levelized cost per savings, the collective levelized cost could be determined as a function of penetration of the measure into the housing set. The aggregate levelized cost for adoption of the EE measure in k houses is as given in Equation (3.5).

Aggregate levelized cost of fuel savings푗 =

푘 푁푃푉푖,푗+퐼푛푖푡푖푎푙 푐표푠푡푖,푗 ∑푖=1 (3.5) 퐴푛푛푢푎푙 푛푎푡푢푟푎푙 푔푎푠 푠푎푣푖푛푔푠푖,푗 (mmBTU)×퐿푖푓푒푡푖푚푒푗

The percentage penetration of this measure into the housing set associated with this aggregate savings is simply 푘⁄푁 × 100, where N is the total number of residences in the considered grouping of residences.

3.4.3 Levelized Cost of Prioritized Investment from Among All Measures Among the

Collection of Houses

In the previous section, the ‘worst-to-first’ strategy described previously was applied to each individual measure. Here, a sequential adoption of measures among the entire collection of measures having the lowest levelized cost of fuel savings is posed.

Figure 12 shows the result of this strategy in terms of the aggregate levelized cost of fuel savings versus % of gas fuel savings. It should be noted that for each house, an upgrade in one measure for the house changes the base state for considering additional measures. For example, the addition of wall insulation in a particular house changes the state of the house.

Savings from a furnace upgrade for this house, for example, would be less. The value of

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the data-based model developed in chapter two which permits fuel consumption estimates were any or all of the measures considered implemented.

It is clear from Figure 12 that a 36% reduction in energy use to the benchmark levelized cost of fuel saving (LCFS) can be realized with levelized costs of $14/mmBTU.

However, a deeper collective savings can increase the cost quickly. For example, for a

LCFS of $20/mmBTU, a 38% natural gas consumption reduction can be realized for the entire community. Moreover, a deeper reduction in energy is increasingly cost negative.

To a degree, the boundary of EE improvement with current can be realized where the point of the slop in LCFS changes dramatically. As a point of comparison, the range of levelized cost of renewable energy investments would be $28/mmBTU for wind energy to $44/mmBTU for solar PV [37]. These values are higher than the McKinsey benchmark by 2-3 times respectively. Thus, this type of analysis in prioritizing can cover not only EE measures, but also where renewable energy investment should become the priority over EE.

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$200

$180 Mckinsey cost $160 $140 $120 $100 $80 $60 $40 $20 $0

0% 10% 20% 30% 40% 50% Levelized Levelized cost fuel savings ($/mmBTU) % Cumulative gas fuel savings

Figure 12. The aggregate levelized cost of fuel savings (LCFS) versus % of gas fuel savings for improvements for all measures considered

Figure 13 provides further detail about how the energy reduction is achieved at various levels of reduction using this ‘worst-to-first’ approach. The contributions from the various

EE measures to the total savings at each savings level is shown in Figure 13. It is clear here that wall insulation addition dominates the savings for the set of homes considered in this study.

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600,000 Water heater upgrade

500,000 Fuarnce upgrade

400,000 Window upgrade Attic Insulation 300,000 addition Wall Insulation 200,000 addition

Energy Energy reduction mmBTU 100,000

0 5% 10% 15% 20% Percentage energy reduction

Figure 13. Provides further detail about how the energy reduction is achieved at various levels using this ‘worst-to-first’ approach

3.4.4 Community-Wide Economic Impacts of Worst to First Investment Option

Table 11 shows the percentage community-wide natural gas savings, annual energy cost savings, investment required for incentivizing each EE measures (i.e. wall insulation, attic insulation, window, furnace, water heater) in the community considered in this study.

For each EE measure, the break-even levelized cost of natural gas savings is $14/mmBTU presented by McKinsey is used as a reference point to calculate the percentile natural gas savings. For example, 36% of cumulative natural gas can be saved with the levelized cost of natural gas savings under than $14/mmBTU for all measures considered.

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Table 11. Savings and investment for achieving LCFS $14/mmBTU from EE measures Fraction Natural Gas Savings Annual Savings ($) Investment ($) Wall Insulation 0.48 $16,024 $117,967 Attic Insulation 0.35 $4,647 $83,514 Window upgrade 0.1 $2,707 $59,703 Furnace upgrade 0.1 $1,846 $26,928 Water heater upgrade 0.1 $1,806 $27,111

The economic benefit that is attributable to the annual savings alone does not appropriate measure of the complete economic impact on overall economic community to start. On the side of getting the cascading economic impact to the community, this study combines an economic input-output framework (EIO) with local detailed data utilizing

IMPLAN (Impact Analysis for PLANning) data. The Leontief Input-Output model is the backbone of this model [38] [39]. It is usually used to analyze the change of the total production (x) by a change of the final demand of commodity (y) by equation 3.6:

-1 Δx = (I - A) Δy (3.6) where A is the n x n inter-industry transaction matrix, y is the monetary amount of the final demand column vector, x is the total monetary industry output column vector. Input-output framework calculates the total supply chain effects of producing goods and services in an economy. The magnitude of the direct, indirect, and total effects is completely dependent on the values of the A matrix (i.e. inter-industry transactions).

Table 11 illustrates the total investment dollars for achieving LCFS $14/mmBTU from EE measures in residential sectors to motivate the local industries to produce more

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EE. For wall insulation, 25% ($29,492), 25% ($29,492), and 50% ($58,984) of total investment dollar are allocated to the “Fiberglass insulation product

(NAICS327993)”, “, foam manufacturing (NAICS

326140)”, and “Construction engineering services (NAICS 541330)” respectively. For window upgrades, 50% ($29,852) is allocated to “Windows and window frame manufacturing (NAICS 326199) and the other 50% ($29,852) is allocated to the

“Construction engineering services (NAICS 541330)”. For furnace upgrades, 50%

($13,464) is allocated to “Electric warm air (forced air) furnaces manufacturing (NAICS

333415)” and the other 50% ($13,464) is allocated to “engineering services (NAICS

541330)”. For hot water heater upgrades, 50% ($13,556) is allocated to the “hot water heaters (including nonelectric), household-type manufacturing (NAICS 335228)” and 50%

($13,556) is allocated to “Contractor services (NAICS 541330)”. For attic insulation, dollar

20% ($16,703), 20% ($16,703), and 60% ($50,109) are allocated to the “Fiberglass insulation product manufacturing (NAICS327993)”, “Thermal insulation, polystyrene foam manufacturing (NAICS 326140)”, and “Construction engineering services (NAICS

541330)” respectively.

Table 12 uses the top 10 sectors out of 400+ industry sectors benefitting from wall improvement to local industries located in the Montgomery County, OH, where the case study homes reside. Overall, it can be clearly seen that the first three industrial sectors increase sales and business services directly from the investment. The indirect and induced economic impacts affected all other industries. Indirect economic impacts are associated with general suppliers who provide raw materials and delivers to the main industry; e.g., fiberglass insulation and manufacturing of thermal insulation industries. It seems to be that

49

these indirect economic impacts do not play a major role in the community addressed in this study. Put in another way, it means that the fiberglass insulation and manufacturing of thermal insulation industries located in this geographical region are importing raw materials and other manufacturing supplies from outside of the region.

Table 12. Top 10 sectors out of several industry sectors benefitting from wall improvement to local industries Description Output Architectural, engineering, and related services $60,882 Fiberglass insulation products manufacturing $29,495 Thermal insulation, polystyrene foam, manufacturing $29,493 Imputed rental activity for owner-occupied dwellings $5,462 Food services and drinking places $3,943 Private hospitals $3,317 Real estate establishments $3,211 Monetary authorities and depository credit intermediation $2,707 activities Wholesale trade businesses $2,657 Telecommunications $2,637 + 430 more sectors…

Another cascading economic impact from the investment of wall insulation additionally is from induced effects. Employees of manufacturing facilities related to insulation may spend more money in society activities, such as renting houses, visiting private hospitals, buying more food and drinking, while increasing the compensation resulting from increased production of EE measures. The same analysis is performed for other measures in the field of energy efficiency (i.e. attic, furnace, window, water heater).

Figure 14 shows the distribution of the direct, indirect and induced effect of all EE measures on investment in society. 50

Figure 14. Breakdown of the local economic impact by EE investment

Table 13 summarizes the direct, indirect, induced impacts along with the first year savings and the cumulative ten year project dollar savings generated from the natural gas savings with the installation of EE measures. For example, with the initial investment of

$117,967 to the wall insulation industry, the community can expect more than $180K total community economic impact (i.e. summation of direct, indirect, and induced). With the installation of wall insulation to the houses covered in this study, the community will get an additional economic benefit $16,024 in the first year through natural gas savings. For next 10 years of project life, the community is expected to avoid $160,235 of natural gas cost. Considering all EE measures, the initial energy efficiency investment of $315K to the

+500 university housing unit located in the Montgomery County, OH could result in total cascading multiplier economic impact of $496K (i.e. direct, indirect, induced together) to this region. In addition, the dollar amount saved from energy efficiency investments will

51

result in additional economic impacts stemming from the annual energy savings of $27K for the lifetime of the considered EE measures.

Table 13. The direct, indirect, induced economic impacts of EE installation to the community Wall Attic Window Furnace Water Total Insulation Insulation Upgrade Upgrade Heater Direct Effect $117,968 $83,515 $59,704 $26,928 $26,928 $315,043 Indirect Effect $27,078 $20,105 $13,117 $6,093 $6,093 $72,486 Induced Effect $37,737 $29,560 $20,806 $10,122 $10,122 $108,347 1st year saving $16,024 $4,647 $2,707 $1,846 $1,846 $27,070 10 years saving $160,235 $46,465 $27,068 $18,462 $18,462 $270,692

The significance of this analysis is especially evident if these results are extrapolated to the City of Dayton, which has just over 45,000 single-family residences of similar construction to the homes in this study. To achieve the targeted 36% energy reduction, the initial energy efficiency investment would be $26.1M. The resulting total cascading multiplier economic impact would be $41.2M. In addition, the dollar amount saved from energy efficiency investments will result in additional economic impacts stemming from the annual energy savings of $2.21M for the lifetime of the considered EE measures.

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3.5 Conclusions

The aim of this study was to implement an energy reduction strategy to achieve the most cost effective energy reduction for a community by starting with the worst residences for energy improvement first. To achieve this aim, a data mining approach is used for developing a single model to estimate natural gas savings and costs among all possible measures for all residences. This provides the possibility of sequential adoption of the most cost-effective energy measures and gives priority to energy reduction measures that take advantage of the building characteristics that can be known or are likely to be known and the energy characteristics of a large number of family houses. The specific case considered addresses hundreds of student residences owned by the University of Dayton in the

Midwest U.S illustrates that an energy (carbon) reduction of 70% by wall insulation additional and 36% by collective improvements can be achieved through a variety of energy characteristics, a worst to first energy reduction strategy appears to be able to achieve results at a levelized cost of less than $14/mmBTU. These results demonstrate the possibility of establishing larger public databases of energy-building characteristics for the implementation of strategic energy reduction strategies in order to maximize energy savings for each cost of implementation.

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CHAPTER 4

CONCLUSIONS AND FUTURE WORK

4.1 Conclusions

An ANN is used for the prediction of the heating energy consumptions of different residential samples from the adoption of individual measures based upon actual building data and not only on energy models. A data mining approach was used to develop a single model that accurately predicts heating energy for all houses given known energy and geometrical characteristics, and using historical energy consumption and weather data. The

ANN model developed to predict savings achieved a high prediction performance

(R2=0.989) that is significantly better than savings estimates based upon physics-based energy models, which as noted have tended to over-predict energy savings. Further, this model was used to predict savings for upgrades to each residence. A sampling of these predictions was shown to match actual savings to within 2.5 percent for most of the measures considered. The practical significance in achieving this degree of accuracy in estimating savings is the potential for gaining confident investment in EE for the future.

While the data used here to develop the models is generally available, there is real potential to inform documentation of building energy characteristics in residential inspections; and potential to utilize the established information created to develop strategic energy reduction efforts by utilities.

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With an ability to accurately predict individual savings in any residence, it then becomes possible to employ a worst-to-first approach, based upon incentivization and implementation of the most cost-effective energy reduction measures among the entire collection of measures within a community first. This strategy, extrapolated to the greater

Dayton region, has been shown capable of delivering greater than $2.21M in annual energy savings for a $26.1M investment. This savings is associated with an overall 36% reduction in energy consumption. The cascading economic impacts of this investment yield a total cascading multiplier economic impact of $41.2M. In addition, the dollar amount saved from energy efficiency investments will result in additional economic impacts stemming from the annual energy savings of $2.21M for the lifetime of the considered EE measures.

Thus, this ‘worst-to-first’ strategy offers a very promising community economic development initiative.

4.2 Future Work

Research is needed in two areas. First, it is essential to work with an entire community to help establish policies to document the energy characteristics of residences in order to enable this approach at scale. Second, it likely will be beneficial to add additional residential information. For example, the number of occupants could be very useful in estimating non-heating and cooling energy. Smart homes, appliances, and WiFi offer additional information which could be exploited to develop energy savings estimates beyond heating (and cooling). Efforts should be completed to investigate the value of using such information to estimate non-weather dependent savings.

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BIBLIOGRAPHY

[1] R. K. Pachauri and L. Meyer, "Cimate Change 2014 Synthesis Report," 2014.

[Online]. Available: https://www.ipcc.ch/pdf/assessment-

report/ar5/syr/SYR_AR5_FINAL_full_wcover.pdf. [Accessed 25 October 2015].

[2] A. 2030, " 2030," 2011. [Online]. Available:

http://architecture2030.org/buildings_problem_why/. [Accessed 14 Aug 2015].

[3] M. Molina, "The Best Value for America’s Energy Dollar: A National Review of

the Cost of Utility Energy Efficiency Programs," March 25, 2014. [Online].

Available:https://in.gov/iurc/files/ACEEE_Attachment_G_ACEEE_Cost_of_Save

d_Energy_Report.pdf. [Accessed 2016].

[4] "rockefellerfoundation,"[Online].Available:http://www.rockefellerfoundation.org/n

ewsroom/deutsche-bank-rockefeller-foundation. [Accessed 20 Dec 2014].

[5] AEE, "Certified Energy Manager," 2013. [Online]. Available:

http://www.ghges.com/marine-solutions/pdfs/CEM%20reconition.pdf. [Accessed

20 November 2016].

[6] EIA, "Energy Information Administration (EIA) - Residential Energy

Consumption Survey (RECS)," EIA, 20 May 2016. [Online]. Available:

https://www.eia.gov/consumption/commercial/reports/2012/preliminary/.

[Accessed 15 October 2016].

56

[7] S. Pigg, "Electricity Use by New Furnaces," 2003. [Online]. Available:

https://www.proctoreng.com/dnld/WIDOE2013.pdf. [Accessed December 2015].

[8] HAAS, "The Energy Institute at Haas," 2015. [Online]. Available:

https://energyathaas.wordpress.com/. [Accessed 8 March 2016].

[9] J. Blanchard, E. Giever, S. Widder and M. Baechler, "Actual and Estimated Energy

Savings Comparison for Deep Energy Retrofits in the Pacific Northwest," Pacific

Northwest national laboratory, October, 2012.

[10] J. Hagerman, "Building America Kick-off," The U.S. Department of Energy -

Energy Efficiency & Renewable Energy, 2010.

[11] R. J. Brecha, A. Mitchell, K. P. Hallinan and J. K. Kissock, "Prioritizing

investment in residential energy efficiency and renewable energy - A case study for

the U.S. Midwest," , vol. 39, no. 5, p. 2982–2992, 2011.

[12] K. P. Hallinan, K. J. Kissock, R. Brecha and M. Austin, "Targeting residential

energy reduction for city utilities using historical electrical utility data and readily

available building data.," in ASHRAE Transactions, 2011.

[13] R. Villoria-Siegert, P. Brodrick, K. P. Hallinan and R. Brecha, "Cost-availability

curves for hierarchical implementation of residential energy-efficiency measures,"

Energy Efficiency, vol. 8, no. 2, p. 267–279, 2014.

[14] B. B. Ekici and U. T. Aksoy, "Prediction of building energy consumption by using

artificial neural networks," Advances in Engineering Software, vol. 40, no. 5, pp.

356-362, 2009.

57

[15] M. Aydinalp, V. I. Ugursal and S. A. Fung, "Modeling of the appliance, lighting,

and spacecooling energy consumptions in the residential sector using neural

networks," Applied energy, vol. 71, pp. 87-110, 2002.

[16] M. Aydinalp-Koksal and V. I. Ugursal, "Comparison of neural network,

conditional demand analysis, and engineering approaches for modeling end-use

energy consumption in the residential sector," Applied energy, vol. 85, no. 4, p.

271–296, 2008.

[17] S. Karatasou, M. Santamouris and V. Geros, "Modeling and predicting building’s

energy use with artificial neural networks: Methods and results," Energy and

Buildings, vol. 38, no. 8, pp. 949-958, 2006.

[18] Y. Chonan, K. Nishida and T. Matsumoto, "Great Energy Predictor Shootout II: A

Bayesian Nonlinear Regression with Multiple Hyperparameters," ASHRAE

Transactions, vol. 102, no. 2, pp. 405-411, 1996.

[19] F. Aqlan, A. Ahmed, K. Srihari and M. T. Khasawneh, "Integrating Artificial

Neural Networks and Cluster Analysis to Assess Energy Efficiency of Buildings,"

in 2014 Industrial and Systems Engineering Research Conference, Canada, 2014.

[20] H. xiang Zhao and F. Magoulès, "A review on the prediction of building energy

consumption," Renewable and eeviews, vol. 16, no. 6, p. 3586–

3592, 2012.

[21] J. Yang, H. Rivard and R. Zmeureanu, "On-line building energy prediction using

adaptive artificial neural networks," Energy and buildings, vol. 37, no. 12, pp.

1250-1259, 2005.

58

[22] K. Jang, E. Bartlett and R. Nelson, "Measuring Retrofit Energy Savings Using

Autoassociative Neural Networks," Fuel and Energy Abstracts, vol. 38, no. 6, pp.

276-276, 1997.

[23] A. H. Neto and F. v. A. S. Fiorelli, "Comparison between detailed model

simulation and artificial neural network for forecasting building energy

consumption," Energy and buildings, vol. 40, no. 12, p. 2169–2176, 2008.

[24] T. Catalina, V. Iordache and B. Caracaleanu, "Multiple regression model for fast

prediction of the heating energy demand," Energy and buildings, vol. 57, p. 302–

312, 2013.

[25] J. Kreider, D. Claridge, P. Curtiss, R. Dodier, J. Haberl and Krarti.M, "Building

Energy Use Prediction and System Identification Using Recurrent Neural

Networks," Journal of Engineering, vol. 117, no. 3, pp. 161-166,

1995.

[26] L. Breiman, "Random forests," Machine Learning, vol. 45, pp. 5-32, 2001.

[27] M. Fels, "PRISM : An introduction," Energy and Buildings, vol. 9, pp. 5-18, 1986.

[28] E. A. Dinsdale, R. A. Edwards, B. A. Bailey, I. Tuba, S. Akhter, K. McNair, R.

Schmieder, N. Apkarian, M. Creek, E. Guan, M. Hernandez, K. Isaacs, C.

Peterson, T. Regh and Ponomarenk, "Multivariate Analysis of Functional

Metagenomes," Dinsdale, E. A., Edwards, R. A., Bailey, B. A., Tuba, I., Akhter, S.,

McNair, K., … Ponomarenko, V. (2013). M Frontiers in Genetics, vol. 4, p. 4:41,

2013.

59

[29] M. Yalcintas and S. Akkurt, "Artificial neural networks applications in building

energy predictions and a case study for tropical climates," INTERNATIONAL

JOURNAL OF ENERGY RESEARCH, vol. 29, p. 891–901, 2005.

[30] F. H. Al-Qahtani and S. F. Crone, "Multivariate k-Nearest Neighbour Regression

for Time Series data -a novel Algorithm for Forecasting UK Electricity Demand,"

in Neural Networks (IJCNN), The 2013 International Joint Conference on, 2013.

[31] K. Gillingham, R. Newell and K. Palmer, "ENERGY EFFICIENCY POLICIES: A

Retrospective Examination," Annual Review of Environment and Resources, vol.

31, pp. 161-192, 2006.

[32] J. G. Koomey, N. C. Martin, M. Brown, L. K. Price and M. D. Levine, "Costs of

reducing carbon emissions: US building sector scenarios," Energy Policy, vol. 26,

pp. 433- 440, 1998.

[33] J. Choi, K. P. Hallinan, K. J. Kissock and R. Brecha, "Economic and

Environmental Impacts of Energy Efficiency Investment on Local Manufacturers,"

in ASME 2015 International Design Engineering Technical Conferences and

Computers and Information in Engineering Conference, Boston, 2015.

[34] S. B. Sadineni, T. M. France and R. F. Boehm, "Economic feasibility of energy

efficiency measures in residential buildings," Renewable Energy, vol. 36, pp. 2925-

2931, 2011.

[35] ENERGY STAR, "ENERGY STAR - Energy Efficient Products," 2017. [Online].

Available: https://www.energystar.gov/products. [Accessed September 2016].

60

[36] H. C. Granade, J. Creyts, A. Derkach, P. Farese, S. Nyquist and K. Ostrowski,

"Unlocking Energy Efficiency in the U.S Economy," McKinsey Global Energy and

Materials, 2009.

[37] EIA, "michigan.gov," 2012. [Online]. Available:

https://www.michigan.gov/documents/energy/Renewable_Energy_Question_3_res

ponse_from_DTE_Consumers_and_MEGA_419474_7.pdf. [Accessed 4 December

2016].

[38] J.-K. Choi, B. R. Bakshi, K. Hubacek and J. Nader, "A sequential input–output

framework to analyze the economic and environmental implications of energy

policies: Gas taxes and fuel subsidies," Applied Energy, vol. 184, p. 830–839,

2016.

[39] J.-K. Choi, D. Morrison, K. P. Hallinan and R. J. Brecha, "Economic and

environmental impacts of community-based residential building energy efficiency

investment," Energy, vol. 78, pp. 877-886, 2014.

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