Automating Energy Management in Green Homes
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Automating Energy Management in Green Homes Nilanjan Banerjee∗ Sami Rollins∗ Kevin Moran University of Arkansas University of San Francisco University of San Francisco Fayetteville, AR San Francisco, CA San Francisco, CA [email protected] [email protected] [email protected] ABSTRACT proved in recent years. We spoke to one user who in- Green homes powered fully or partially by renewable sources stalled solar panels at his home in 2002 and who, in order such as solar are becoming more widely adopted, however to log the performance of the system, manually recorded energy management strategies in these environments are lack- readings from a control panel on a daily basis over a ing. This paper presents the first results of a study that ex- period of several months. Another user, however, who plores home automation techniques for achieving better uti- is in the process of installing solar panels, reports that lization of energy generated by renewable technologies. First, he will have access to a web-based service that reports using a network of off-the-shelf sensing devices, we observe overall energy generation and consumption at his home. that energy generation and consumption in an off-grid home There has also been a significant increase in the number is both variable and predictable. Moreover, we find that re- of devices, such as the WattsUpMeter [11], that allow active energy management techniques are insufficient to pre- monitoring of power consumption, as well as services vent critical battery situations. We then present a recommen- such as Google PowerMeter [3, 6, 9] that provide users dation based system for helping users to achieve better uti- with convenient access to such information. These tech- lization of resources. Our study demonstrates the feasibil- nologies, however, do little more than provide access to ity of three recommendation components: an early warning fine-grained information. The user must still attentively system that allows users of renewable technologies to make monitor readings, aggregate the information collected by more conservative decisions when energy harvested is pre- a diverse set of sensing devices, and decide how to use dicted to be low; a task rescheduling system that advises the information to make smart decisions about energy users when high-power appliances such as clothes dryers consumption. should be run to optimize overall energy utilization; and an This paper presents the first results of a study that energy conservation system that identifies sources of energy explores automated techniques for changing energy con- waste and recommends more conservative usage. sumption patterns to achieve better utilization of avail- able resources in green homes. Through an empirical case study, we derive four key insights that demonstrate 1. INTRODUCTION the need for and the feasibility of automated energy management in green homes. First, we explore how en- Homes that derive part or all of their energy from ergy is both generated and consumed in an off-grid renewable sources, such as solar and wind, are becoming home entirely powered by solar panels. Using a network more widely adopted. Though early adopters of renew- of off-the-shelf sensing devices, we observe that energy able technologies often report that their motivation for generation and consumption in the home is both vari- adoption was a desire to be more environmentally re- able and predictable. Moreover, we observe that the sponsible, new pricing models such as leases have made residents in the home use reactive energy management renewables more affordable. As a result, renewables have techniques—they wait until the energy supply is low become a more attractive option for people who are also before taking action to reduce overall energy consump- interested in reducing their energy costs. In the US, the tion. This approach, we find, is not sufficient to pre- number of grid-tied homes with photovoltaic installa- vent the critical situation in which a generator must be tions grew by 40% in 2009 [7,15]. The growth was largely used to power the home. Based on our observations, we fueled by decreasing cost of solar technologies and in- propose a recommendation-based system that monitors crease in incentives offered to consumers who install the energy harvested and consumed and provides users with technology. These trends are unlikely to reverse [15]. feedback and advice on how to adjust energy consump- Technology and services that help consumers to man- tion patterns via a smartphone application. Our study age the energy generated by their renewables have im- demonstrates the feasibility of three recommendation *lead co-authors listed in alphabetical order 1 Charge Controller Mate Remote Solar Panels Inverter Lead Acid Batteries Primary monitor 1: The monitoring setup at the off-grid, solar-powered home. The primary monitoring device is a FitPC tethered to a Mate remote control that profiles the inverter. It is possible to access the solar charging and energy expended data over a standard RS232 interface using custom software. components: an early warning system that allows users transform DC current to AC, which powers the appli- of renewable technologies to make more conservative ances at home. We have augmented the above setup decisions when energy harvested is predicted to be low; with a monitoring station that consists of a low-power a task rescheduling system that advises users when high- FitPC [5]. A custom software tool on the FitPC periodi- power appliances such as clothes dryers should be run to cally collects data from a controller device (called the optimize overall energy utilization; and an energy con- MATE remote [13]) that collects data from the inverter. servation system that identifies sources of energy waste Data at the rate of one sample per minute is captured and recommends more conservative usage. over the RS232 interface and written to an append-only This exploratory study focuses on an off-grid, solar log. Each sample consists of the instantaneous residual home in Arkansas. We recognize that off-grid users are battery voltage (in Volts) and the energy consumed by an extremely small segment of the population, and our the house (in KWh). The amount of energy scavenged goal is not to develop technology that will only be useful from the panels can be calculated using these values in an off-grid home. There are two significant benefits and the maximum battery capacity. At this time the log to working in this space, however. First, these users are files are manually transferred by the house occupants. at the extreme end of the spectrum when it comes to In addition to monitoring the cumulative energy con- conservation. Understanding and classifying the manual sumption for the home, we have deployed networked techniques they have developed for demand-response can WattsUpMeters to monitor individual appliances. We inform the recommendations we can make to other grid- currently monitor a television and a refrigerator. We tied, or even standard grid users. Second, off-grid users have collected data over a period of 55 days (14 days are likely to be enthusiastic early adopters of our technol- during the summer of 2010 and 41 days in Nov-Dec ogy, helping us to validate and improve our techniques 2010) and plan to continue collecting data over the next without a significant barrier to adoption. two years. 2. AN OFF-GRID SOLAR HOME 3. DATA ANALYSIS To understand the semantics governing energy uti- Analysis of the data collected by our monitoring infras- lization in green homes, we have deployed a suite of tructure highlights four key findings that have informed monitoring tools in a solar-powered home in Fayetteville, the design of our automated energy management system. Arkansas. The goal of the monitoring infrastructure is to drive measurement-driven analysis to understand how Traditional energy management techniques are users in solar-powered houses interact with energy. The insufficient in off-grid homes. house is occupied by a couple; one occupant works for First, we consider whether established norms of en- the University of Arkansas and the other occupies the ergy management, for example the 7PM-7AM policy that house most of the day. recommends users run important household appliances Our deployment is shown in Figure 1. The house is between 7PM and 7AM when the demand for power is located several kilometers from the city and is powered low, are appropriate for green homes. Figure 2 shows by 8 Kyocera 120 W solar panels [10]. The solar panels the total energy consumption in the off-grid house at drive 24 2V 840 Ah Lead Acid batteries (two banks different times of the day over 41 winter days. While the of 12 batteries = 24V) through a FlexMax 60 charge energy consumption varies considerably over 24 hours, controller [12]. A VFX3524 Power Inverter is used to we find that a large portion of energy is consumed be- 2 300 30 250 30 29 200 28 28 150 27 100 26 26 Battery Voltage (V) 25 Battery Voltage (V) Energy Consumption (Wh) 50 24 24 0 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 5 10 15 20 25 30 35 40 Hour number (from 12:00 AM) Day Day 2: Energy consumption during dif- 3: Variability in battery voltage in 4: Variability in battery voltage in ferent hours of the day averaged the off-grid home across a single the off-grid home across a single over 41 winter days. The error- day over 14 summer days in 2010. day over 41 winter days in 2010. bars indicate the standard deviation across 41 days. tween 10AM-8PM (10-20 on the x-axis). Conversations the voltage of his batteries and reduces consumption, for with our subject indicate that consumption is primar- example by switching off lights, when the voltage drops ily driven by residual energy in their batteries.