A data-driven method for demand response baselining and strategy evaluation is presented. Using meter and weather data along with set-point schedule information, we use an ensemble of regression trees to learn non-parametric data-driven models for predicting the power consumption of the building. This model can be used for evaluating demand response strategies in real-time, without having to learn complex models of the building. The methods have been integrated in an open-source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager by advising on which control actions should be during a demand response event. We provide a case study using data from a large commercial vistural test-bed building to evaluate the performance of the DR-Advisor tool. Keywords: demand response, regression trees, machine learnin

Behl, Madhur

Mangharam, Rahul

Nghiem, Truong X

English

BEHL, Madhur

NGHIEM, Truong

MANGHARAM, Rahul

Infoscience - École polytechnique fédérale de Lausanne

DR-ADVISOR: A DATA-DRIVEN DEMAND RESPONSERECOMMENDER SYSTEMMadhur Behl1, Truong X. Nghiem2; Rahul Mangahram11Electrical and Systems Engineering, University of Pennsylvania, USA.,{mbehl,rahulm}@seas.upenn.edu2Laboratoire d’ Automatique, E´cole Polytechnique Fe´de´rale de Lausanne, Switzerland,xuan.nghiem@epfl.chABSTRACTA data-driven method for demand response baselining and strategy evaluation is pre-sented. Using meter and weather data along with set-point schedule information, we usean ensemble of regression trees to learn non-parametric data-driven models for predictingthe power consumption of the building. This model can be used for evaluating demandresponse strategies in real-time, without having to learn complex models of the building.The methods have been integrated in an open-source tool called DR-Advisor, which actsas a recommender system for the building’s facilities manager by advising on which con-trol actions should be during a demand response event. We provide a case study usingdata from a large commercial vistural test-bed building to evaluate the performance ofthe DR-Advisor tool. Keywords: demand response, regression trees, machine learningINTRODUCTIONIn 2013, a report by the National Climate Assessment provided evidence that the mostrecent decade was the nations warmest on record [1] and experts predict that temperaturesare only going to rise. Every year an overstressed electric grid faces increasing challengesto operate homes and buildings. Heat waves in summer and polar vortexes in winter aregrowing longer in duration which could result in energy shortages and blackouts.To improve reliability of the electricity grid, across the United States, electric utilitiesand independent system operators (ISOs) are devoting increasing attention and resourcesto demand response (DR) [2]. While energy efficiency is a prominent component of grow-ing efforts to supply affordable, reliable and clean electric power; most utilities and systemoperators are increasingly turning to demand response as a cost effective and environmen-tally responsible way to serve peak load. Potential peak reduction from demand responsemarkets in U.S. increased by 2, 451 MW or 9.3 percent to a total of 28, 503 MW from2012 to 2013 [3]. The estimated revenue for economic and load management DR marketswith PJM alone is about $700 million [4].Buildings, particularly large commercial buildings, are large consumers of electricityand a significant contributor to peak load conditions in the grid. Their electricity de-mands are often sensitive to weather conditions, which can results in peaks in their powerconsumption on an extremely hot or an extremely cold day. Such customers are alsoincreasingly looking to DR programs to help manage energy costs.Demand response programs are designed to elicit changes in customers electric usagepatterns. Some types of demand response, implemented through approved utility tariffsor through contractual arrangements, vary the price of electricity over time to motivatecustomers to change their consumption patterns; this approach is termed price-baseddemand response.CISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerland 401Historical	  Data	  Temperature Humidity Wind Time of Day Day of Week Schedule Chilled Water Temp. Lighting Zone Temperature Power (kW) Estimate Baseline Power Consumption Baseline	  Tree	   DR	  Evalua4on	  Tree	  DR	  Synthesis	  Tree	  24hr	  Predic4on	  	  Tree	  Build	  a	  Family	  of	  Regression	  Trees	  Choose the best DR Strategy Compute the best set-points for a DR Strategy DR	  Baseline	   DR	  Evalua4on	   DR	  Synthesis	  Figure 1: DR-Advisor ArchitectureDR-Advisor is a data-driven tool for demand response baselining, strategy evaluationand synthesis. Using meter and weather data along with set-point and schedule informa-tion, it uses a family of regression trees to learn non-parametric data-driven models forpredicting the power consumption of the building (Figure 1). These models can be usedfor real-time demand response strategy evaluation, without having to learn complex mod-els of the building. DR-Advisor acts as a recommender system for the building’s facilitiesmanager by advising on which control actions should be taken during a DR event.1. We demonstrate the benefit of using regression trees based methods for estimatingdemand response baseline power consumption and for evaluating pre-determineddemand response strategies in real time. The use of such methods for demandresponse problems is novel.2. We evaluate and compare the performance of several tree based methods on a De-partment of Energy’s (DoE) Large Commercial Reference Building using actualmeteorological data.3. The biggest contribution of this work is the fusion of a family of regression trees intoDR-Advisor, a simple and highly interpretable open source tool. It eliminates thecost of time and effort required to build and tune high fidelity models of buildingsfor DR.Problem DefinitionThe two most popular approaches to respond to a demand response event include rulebased and model based DR strategies. In a rule based demand response strategy, differentlevels of curtailment are achieved by following a pre-programmed strategy. Such a DRstrategy can include fixed setbacks for thermostat set-points, pre-determined dimming oflights and temporarily switching off large equipment e.g., elevators. Model based design402 CISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerlandfor DR involves explicitly mathematically modeling the building and its equipment inorder to predict the overall power consumption. However, creating and learning suchhigh fidelity models is both cost and time prohibitive. This is because the user expertise,time, and associated costs required to develop a software model of a single building isquite large.In this paper, we focus on two challenging problems of demand response1. DR baseline prediction: A baseline is an estimate of the electricity that wouldhave been consumed by a customer in the absence of a demand response event. Themeasurement and verification of demand response is the most critical componentof any DR program since any curtailment can only be measured relative to theestimate of the demand response baseline.2. Real-time DR strategy evaluation: This is the problem of choosing good DRstrategies from a pre-determined set of strategies, in real-time. During a DR eventnotification, there are several options available to a buildings facilities manager inthe form of a control actions. These may include setbacks in the zone temperatureset-point, increasing supply air temperature and chilled water temperature set-point,dimming or turning off lights, decreasing duct pressure set-points and switching offno-essential electrical load. However, there could be several such fixed rules orstrategies. With our tree based models, we can predict the response of the buildingdue to any strategy, and hence, choose the best action during the DR event.In key to solving both the problems is the ability to predict the power consumption ofthe building in real-time.DR-ADVISOR: DATA-DRIVEN DEMAND RESPONSERegression trees are decision tress which predict responses to data. Regression treesbelong to the class of recursive partitioning algorithms. At each node of the tree, wecheck the value of one the inputs (or features) Xi and depending of the (binary) answerwe continue to the left or to the right subbranch. When we reach a leaf we will obtainthe prediction of the response Y. The seminal algorithm for learning regression treesfrom data is the CART algorithm as described in [5]. Contrary to linear or polynomialregression which are global models (the predictive formula is supposed to hold in theentire data space), trees try to partition the data space into small enough parts wherewe can apply a simple different model on each part. They are conceptually simple yetpowerful. Regression trees offer several advantages in addition to being simple, whichmake them suitable for solving the challenges of demand response and building modeling.We list some of these advantages here:1. Trees require very low computation power, both running time and storage require-ments.2. Trees can easily handle the case where the data has lots of features which interactin complicated and nonlinear ways. the predictor variables themselves can be of anycombination of continuous, discrete and categorical variables.3. Sometimes, data has missing predictor values in some or all of the predictor vari-ables. This is especially true for buildings, where sensor data streams fail frequentlydue to faulty sensors or faulty communication links. By design, regression treescan handle missing data better than most algorithms through the use of surrogatevariables.4. Tree based models are generally not affected by outliers but regression based modelsare.CISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerland 403Figure 2: DR-Advisor graphical user interface5. Trees are highly interpretable algorithms. Complex building models go through along calculation routine and involve too many factors. It is not easy for a humanengineer to judge if the operation/decision is correct or not or how it was generatedin the first place. Trees only involve simple if this then that rules which are veryeasy to understand.Ensemble MethodsThe problem with trees is their high variance and that they can over fit the data. It isthe price to be paid for estimating a simple, tree-based structure from the data. Whilepruning and cross validation can help reduce over fitting, in DR-Advisor,we use ensemblemethods for growing more stable trees. The goal of ensemble methods is to combine thepredictions of several base estimators built with a given learning algorithm in order toimprove generalizability and robustness over a single estimator. Two families of ensemblemethods are usually distinguished: (a) In averaging methods, the driving principle is tobuild several estimators independently and then to average their predictions. On average,the combined estimator is usually better than any of the single base estimator becauseits variance is reduced. (b) By contrast, in boosting methods, base estimators are builtsequentially and one tries to reduce the bias of the combined estimator. The motivationis to combine several weak models to produce a powerful ensemble. The DR-advisor toolused a combination of cross validated trees, random forest and boosted regression trees asthe underlying ensemble methods. For a more comprehensive review we refer the readerto [6].CASE STUDYThe building under consideration is the DOE Commercial Reference Building simulatedin EnergyPlus [7] This virtual test-bed is a large 12 story office building consisting of 73zones with a total area of 500, 000 sq ft. There are upto 2, 397 people in the building duringpeak occupancy. The building has 2 electric water-cooled chillers, variable air volume(VAV) supply air terminals with reheat and plenum zones and a single gas based boiler.During peak load conditions the building can consume up to 1.6 MW of power. EnergyPlus404 CISBAT 2015 - September 9-11, 2015 - Lausanne, SwitzerlandDR Event 16:55 17:00 17:05 17:10 17:15 17:20 17:25 17:30 17:35 17:40 17:45 17:50 17:55 18:00 18:05Power Consumption (MW)0.40.60.811.21.41.6Baseline Estimates for July 17th Peak Load Contribution DR EventGround TruthSingle TreeCV TreeBoosted TreeRandom ForestM5 ModelFigure 3: Comparison between the actual power consumption and the baseline predictionfor July 17th, 2013; a peak load contribution day. The DR event is from 1700-1800 hrs.provides typical meteorological year data files for many sites which are generated asaverages of different weather characteristics across the past 15-30 years. However, forthe purposes of the simulation we use Actual Meteorological Year (AMY ) data fromChicago for the years 2012 and 2013.The data that we use can be divided into three different categories as described below:1. Weather data, which includes measurements of the dry bulb temperature, wet bulbtemperature, relative humidity and wind conditions.2. Schedule data, which includes fixed temperature set-points schedules of chilled watersupply, supply air temperature and zone air temperature on the HVAC side andlighting schedules.3. Building data, which includes the measurements of zone temperature, lighting, sup-ply air and water temperatures, power consumption etc.In addition to these data sets we also train on engineered features like the time ofday and the day of week.DR BaselineOn July 17, 2013 a demand response event occurred across the PJM ISO from 1700hrs to 1600 hrs. We estimate the baseline power consumption of the office building forthe DR event for July 17, 2013. The result of this comparison is shown in Figure 3. Inaddition to evaluating a single regression tree, we implement and evaluate the performanceof cross validated trees and the random forest and the boosted regression tree ensemblemethods as well. The lowest root mean square error obtained in this case is only 12 kWon an average consumption of 0.62 MW, which corresponds to a normalised root meansquare (NRMSE) of only 2.01%. Using the ensemble methods, the DR-Advisor is ableto accurately predict the baseline consumption of the building using just weather andschedule data, which require little to no sensor installations at the building site.DR Strategy EvaluationAs stated earlier, the challenge is DR strategy evaluation is to predict the power con-sumption profile of the building in real-time due to a fixed policy. The following demandresponse strategy is evaluated. Upon receiving the notification of the DR event at 1600hrs, the zone air temperature set-point for all the zones is increased from a nominal valueof 24◦C by 2◦ to 26◦C. The chilled water supply temperature set-point is increased from6.7◦C by 1.5◦ to 7.2◦C. At the beginning of the event at 1700 hrs, the zone air temperatureset-point is further increased by 2◦ and the chilled water supply temperature set-pointCISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerland 405July 17, 201300:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00Set-Point Temperature °C051015202530NotificationStartEndTemperature Set-Point Reset DR Curtailment StrategyZone Air Temperature Set-PointChilled Water Supply Temperature Set-PointJuly 17 DR Event15:45 16:00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 18:1500.20.40.60.811.21.41.6DR Event Strategy Evaluation for 17 July, 2013Ground TruthSingle TreeCV TreeBoosted TreeRandom ForestM5 ModelNotification Start EndFigure 4: Left:Rule based demand response temperature set-point reset strategy executedfor July 17, 2013. Right: Comparison between the actual power consumption and thepredicted power for July 17th, 2013; There is a DR event from 1700-1800 hrs.is increased by another 1.5◦. This fixed, rule-based strategy is shown in Figure 4(left).The predicted response of the building compared to the actual response due to the fixedstrategy is shown in Figure 4(right). We obtain an error of 6.23% for predicting the powerconsumption of the building in real time during a demand response event.DISCUSSIONWe presented DR-Advisor, a data-driven tool which acts as a recommender system fordemand response baselining and strategy evaluation. We evaluate the performance ofDR-ADvisor on a large scale DOE reference commercial building , using actual meteoro-logical year data. We show how the tree based methods can achieve a good predictionaccuracy of 3− 6% on average for all the cases. The biggest advantage of DR-Advisor isthat it completely bypasses the need to build high fidelity models of buildings e.g., withEnergyPlus or with RC networks. Another major advantage of DR-Advisor is that themodels it builds are highly interpretable and simple, an attribute which is often com-pletely neglected in the design of such algorithms. These advantages combines with thefact that the tree based methods can achieve high prediction accuracies, make DR-Advisora very alluring tool for evaluating and planning DR curtailment responses. DR-Advisor(Figure 2) is being developed into a free and open source tool. Since DR-Advisor is adata-driven approach, it can be easily scaled to multiple buildings and can be used forcampus-wide demand response which is a part of our on-going work.REFERENCES1. Melillo, J. M., Richmond, T., and Yohe, G. W.: Climate change impacts in the unitedstates: the third national climate assessment. US Global change research program, 841,2014.2. Goldman, C.: Coordination of energy efficiency and demand response. Lawrence Berke-ley National Laboratory, 2010.3. Commission, F. E. R.: Assessment of demand response and advanced metering. 2008.4. pjm interconnection: 2014 demand response operations markets activity report. 2014.5. Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A.: Classification and regres-sion trees. CRC press, 1984.6. Breiman, L.: Random forests. Machine learning, 45(1):5–32, 2001.7. Deru, M., Field, K., Studer, D., et al.: U.s. department of energy commercial referencebuilding models of the national building stock. 2010.406 CISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerland

DR-Advisor: A data driven demand response recommender system

Kosmopolis

DR-Advisor: A Data Driven Demand Response Recommender System

ScholarlyCommons@Penn

University of PennsylvaniaScholarlyCommonsReal-Time and Embedded Systems Lab (mLAB) School of Engineering and Applied Science9-2015DR-Advisor: A Data Driven Demand ResponseRecommender SystemMadhur BehlUniversity of Pennsylvania, mbehl@seas.upenn.eduTruong X. NghiemÉcole Polytechnique Fédérale de Lausanne, xuan.nghiem@epfl.chRahul MangharamUniversity of Pennsylvania, rahulm@seas.upenn.eduFollow this and additional works at: http://repository.upenn.edu/mlab_papersPart of the Computer Engineering Commons, and the Electrical and Computer EngineeringCommonsThis paper is posted at ScholarlyCommons. http://repository.upenn.edu/mlab_papers/78For more information, please contact repository@pobox.upenn.edu.Recommended CitationMadhur Behl, Truong X. Nghiem, and Rahul Mangharam, "DR-Advisor: A Data Driven Demand Response Recommender System",Proceedings of International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale (EPFL-CONF-213354), 401-406. September 2015.DR-Advisor: A Data Driven Demand Response Recommender SystemAbstractA data-driven method for demand response baselining and strategy evaluation is presented. Using meter andweather data along with set-point schedule information, we use an ensemble of regression trees to learn non-parametric data-driven models for predicting the power consumption of the building. This model can be usedfor evaluating demand response strategies in real-time, without having to learn complex models of thebuilding. The methods have been integrated in an open-source tool called DR-Advisor, which acts as arecommender system for the building’s facilities manager by advising on which control actions should beduring a demand response event. We provide a case study using data from a large commercial vistural test-bedbuilding to evaluate the performance of the DR-Advisor tool. Keywords: demand response, regression trees,machine learningKeywordsData-driven, CPS, demand response, buildings, modeling, controlDisciplinesComputer Engineering | Electrical and Computer EngineeringCommentsThis conference paper is available at ScholarlyCommons: http://repository.upenn.edu/mlab_papers/78DR-ADVISOR: A DATA-DRIVEN DEMAND RESPONSERECOMMENDER SYSTEMMadhur Behl1, Truong X. Nghiem2; Rahul Mangahram11Electrical and Systems Engineering, University of Pennsylvania, USA.,{mbehl,rahulm}@seas.upenn.edu2Laboratoire d’ Automatique, E´cole Polytechnique Fe´de´rale de Lausanne, Switzerland,xuan.nghiem@epfl.chABSTRACTA data-driven method for demand response baselining and strategy evaluation is pre-sented. Using meter and weather data along with set-point schedule information, we usean ensemble of regression trees to learn non-parametric data-driven models for predictingthe power consumption of the building. This model can be used for evaluating demandresponse strategies in real-time, without having to learn complex models of the building.The methods have been integrated in an open-source tool called DR-Advisor, which actsas a recommender system for the building’s facilities manager by advising on which con-trol actions should be during a demand response event. We provide a case study usingdata from a large commercial vistural test-bed building to evaluate the performance ofthe DR-Advisor tool. Keywords: demand response, regression trees, machine learningINTRODUCTIONIn 2013, a report by the National Climate Assessment provided evidence that the mostrecent decade was the nations warmest on record [1] and experts predict that temperaturesare only going to rise. Every year an overstressed electric grid faces increasing challengesto operate homes and buildings. Heat waves in summer and polar vortexes in winter aregrowing longer in duration which could result in energy shortages and blackouts.To improve reliability of the electricity grid, across the United States, electric utilitiesand independent system operators (ISOs) are devoting increasing attention and resourcesto demand response (DR) [2]. While energy efficiency is a prominent component of grow-ing efforts to supply affordable, reliable and clean electric power; most utilities and systemoperators are increasingly turning to demand response as a cost effective and environmen-tally responsible way to serve peak load. Potential peak reduction from demand responsemarkets in U.S. increased by 2, 451 MW or 9.3 percent to a total of 28, 503 MW from2012 to 2013 [3]. The estimated revenue for economic and load management DR marketswith PJM alone is about $700 million [4].Buildings, particularly large commercial buildings, are large consumers of electricityand a significant contributor to peak load conditions in the grid. Their electricity de-mands are often sensitive to weather conditions, which can results in peaks in their powerconsumption on an extremely hot or an extremely cold day. Such customers are alsoincreasingly looking to DR programs to help manage energy costs.Demand response programs are designed to elicit changes in customers electric usagepatterns. Some types of demand response, implemented through approved utility tariffsor through contractual arrangements, vary the price of electricity over time to motivatecustomers to change their consumption patterns; this approach is termed price-baseddemand response.CISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerland 401Historical	  Data	  Temperature Humidity Wind Time of Day Day of Week Schedule Chilled Water Temp. Lighting Zone Temperature Power (kW) Estimate Baseline Power Consumption Baseline	  Tree	   DR	  Evalua4on	  Tree	  DR	  Synthesis	  Tree	  24hr	  Predic4on	  	  Tree	  Build	  a	  Family	  of	  Regression	  Trees	  Choose the best DR Strategy Compute the best set-points for a DR Strategy DR	  Baseline	   DR	  Evalua4on	   DR	  Synthesis	  Figure 1: DR-Advisor ArchitectureDR-Advisor is a data-driven tool for demand response baselining, strategy evaluationand synthesis. Using meter and weather data along with set-point and schedule informa-tion, it uses a family of regression trees to learn non-parametric data-driven models forpredicting the power consumption of the building (Figure 1). These models can be usedfor real-time demand response strategy evaluation, without having to learn complex mod-els of the building. DR-Advisor acts as a recommender system for the building’s facilitiesmanager by advising on which control actions should be taken during a DR event.1. We demonstrate the benefit of using regression trees based methods for estimatingdemand response baseline power consumption and for evaluating pre-determineddemand response strategies in real time. The use of such methods for demandresponse problems is novel.2. We evaluate and compare the performance of several tree based methods on a De-partment of Energy’s (DoE) Large Commercial Reference Building using actualmeteorological data.3. The biggest contribution of this work is the fusion of a family of regression trees intoDR-Advisor, a simple and highly interpretable open source tool. It eliminates thecost of time and effort required to build and tune high fidelity models of buildingsfor DR.Problem DefinitionThe two most popular approaches to respond to a demand response event include rulebased and model based DR strategies. In a rule based demand response strategy, differentlevels of curtailment are achieved by following a pre-programmed strategy. Such a DRstrategy can include fixed setbacks for thermostat set-points, pre-determined dimming oflights and temporarily switching off large equipment e.g., elevators. Model based design402 CISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerlandfor DR involves explicitly mathematically modeling the building and its equipment inorder to predict the overall power consumption. However, creating and learning suchhigh fidelity models is both cost and time prohibitive. This is because the user expertise,time, and associated costs required to develop a software model of a single building isquite large.In this paper, we focus on two challenging problems of demand response1. DR baseline prediction: A baseline is an estimate of the electricity that wouldhave been consumed by a customer in the absence of a demand response event. Themeasurement and verification of demand response is the most critical componentof any DR program since any curtailment can only be measured relative to theestimate of the demand response baseline.2. Real-time DR strategy evaluation: This is the problem of choosing good DRstrategies from a pre-determined set of strategies, in real-time. During a DR eventnotification, there are several options available to a buildings facilities manager inthe form of a control actions. These may include setbacks in the zone temperatureset-point, increasing supply air temperature and chilled water temperature set-point,dimming or turning off lights, decreasing duct pressure set-points and switching offno-essential electrical load. However, there could be several such fixed rules orstrategies. With our tree based models, we can predict the response of the buildingdue to any strategy, and hence, choose the best action during the DR event.In key to solving both the problems is the ability to predict the power consumption ofthe building in real-time.DR-ADVISOR: DATA-DRIVEN DEMAND RESPONSERegression trees are decision tress which predict responses to data. Regression treesbelong to the class of recursive partitioning algorithms. At each node of the tree, wecheck the value of one the inputs (or features) Xi and depending of the (binary) answerwe continue to the left or to the right subbranch. When we reach a leaf we will obtainthe prediction of the response Y. The seminal algorithm for learning regression treesfrom data is the CART algorithm as described in [5]. Contrary to linear or polynomialregression which are global models (the predictive formula is supposed to hold in theentire data space), trees try to partition the data space into small enough parts wherewe can apply a simple different model on each part. They are conceptually simple yetpowerful. Regression trees offer several advantages in addition to being simple, whichmake them suitable for solving the challenges of demand response and building modeling.We list some of these advantages here:1. Trees require very low computation power, both running time and storage require-ments.2. Trees can easily handle the case where the data has lots of features which interactin complicated and nonlinear ways. the predictor variables themselves can be of anycombination of continuous, discrete and categorical variables.3. Sometimes, data has missing predictor values in some or all of the predictor vari-ables. This is especially true for buildings, where sensor data streams fail frequentlydue to faulty sensors or faulty communication links. By design, regression treescan handle missing data better than most algorithms through the use of surrogatevariables.4. Tree based models are generally not affected by outliers but regression based modelsare.CISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerland 403Figure 2: DR-Advisor graphical user interface5. Trees are highly interpretable algorithms. Complex building models go through along calculation routine and involve too many factors. It is not easy for a humanengineer to judge if the operation/decision is correct or not or how it was generatedin the first place. Trees only involve simple if this then that rules which are veryeasy to understand.Ensemble MethodsThe problem with trees is their high variance and that they can over fit the data. It isthe price to be paid for estimating a simple, tree-based structure from the data. Whilepruning and cross validation can help reduce over fitting, in DR-Advisor,we use ensemblemethods for growing more stable trees. The goal of ensemble methods is to combine thepredictions of several base estimators built with a given learning algorithm in order toimprove generalizability and robustness over a single estimator. Two families of ensemblemethods are usually distinguished: (a) In averaging methods, the driving principle is tobuild several estimators independently and then to average their predictions. On average,the combined estimator is usually better than any of the single base estimator becauseits variance is reduced. (b) By contrast, in boosting methods, base estimators are builtsequentially and one tries to reduce the bias of the combined estimator. The motivationis to combine several weak models to produce a powerful ensemble. The DR-advisor toolused a combination of cross validated trees, random forest and boosted regression trees asthe underlying ensemble methods. For a more comprehensive review we refer the readerto [6].CASE STUDYThe building under consideration is the DOE Commercial Reference Building simulatedin EnergyPlus [7] This virtual test-bed is a large 12 story office building consisting of 73zones with a total area of 500, 000 sq ft. There are upto 2, 397 people in the building duringpeak occupancy. The building has 2 electric water-cooled chillers, variable air volume(VAV) supply air terminals with reheat and plenum zones and a single gas based boiler.During peak load conditions the building can consume up to 1.6 MW of power. EnergyPlus404 CISBAT 2015 - September 9-11, 2015 - Lausanne, SwitzerlandDR Event 16:55 17:00 17:05 17:10 17:15 17:20 17:25 17:30 17:35 17:40 17:45 17:50 17:55 18:00 18:05Power Consumption (MW)0.40.60.811.21.41.6Baseline Estimates for July 17th Peak Load Contribution DR EventGround TruthSingle TreeCV TreeBoosted TreeRandom ForestM5 ModelFigure 3: Comparison between the actual power consumption and the baseline predictionfor July 17th, 2013; a peak load contribution day. The DR event is from 1700-1800 hrs.provides typical meteorological year data files for many sites which are generated asaverages of different weather characteristics across the past 15-30 years. However, forthe purposes of the simulation we use Actual Meteorological Year (AMY ) data fromChicago for the years 2012 and 2013.The data that we use can be divided into three different categories as described below:1. Weather data, which includes measurements of the dry bulb temperature, wet bulbtemperature, relative humidity and wind conditions.2. Schedule data, which includes fixed temperature set-points schedules of chilled watersupply, supply air temperature and zone air temperature on the HVAC side andlighting schedules.3. Building data, which includes the measurements of zone temperature, lighting, sup-ply air and water temperatures, power consumption etc.In addition to these data sets we also train on engineered features like the time ofday and the day of week.DR BaselineOn July 17, 2013 a demand response event occurred across the PJM ISO from 1700hrs to 1600 hrs. We estimate the baseline power consumption of the office building forthe DR event for July 17, 2013. The result of this comparison is shown in Figure 3. Inaddition to evaluating a single regression tree, we implement and evaluate the performanceof cross validated trees and the random forest and the boosted regression tree ensemblemethods as well. The lowest root mean square error obtained in this case is only 12 kWon an average consumption of 0.62 MW, which corresponds to a normalised root meansquare (NRMSE) of only 2.01%. Using the ensemble methods, the DR-Advisor is ableto accurately predict the baseline consumption of the building using just weather andschedule data, which require little to no sensor installations at the building site.DR Strategy EvaluationAs stated earlier, the challenge is DR strategy evaluation is to predict the power con-sumption profile of the building in real-time due to a fixed policy. The following demandresponse strategy is evaluated. Upon receiving the notification of the DR event at 1600hrs, the zone air temperature set-point for all the zones is increased from a nominal valueof 24◦C by 2◦ to 26◦C. The chilled water supply temperature set-point is increased from6.7◦C by 1.5◦ to 7.2◦C. At the beginning of the event at 1700 hrs, the zone air temperatureset-point is further increased by 2◦ and the chilled water supply temperature set-pointCISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerland 405July 17, 201300:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00Set-Point Temperature °C051015202530NotificationStartEndTemperature Set-Point Reset DR Curtailment StrategyZone Air Temperature Set-PointChilled Water Supply Temperature Set-PointJuly 17 DR Event15:45 16:00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 18:1500.20.40.60.811.21.41.6DR Event Strategy Evaluation for 17 July, 2013Ground TruthSingle TreeCV TreeBoosted TreeRandom ForestM5 ModelNotification Start EndFigure 4: Left:Rule based demand response temperature set-point reset strategy executedfor July 17, 2013. Right: Comparison between the actual power consumption and thepredicted power for July 17th, 2013; There is a DR event from 1700-1800 hrs.is increased by another 1.5◦. This fixed, rule-based strategy is shown in Figure 4(left).The predicted response of the building compared to the actual response due to the fixedstrategy is shown in Figure 4(right). We obtain an error of 6.23% for predicting the powerconsumption of the building in real time during a demand response event.DISCUSSIONWe presented DR-Advisor, a data-driven tool which acts as a recommender system fordemand response baselining and strategy evaluation. We evaluate the performance ofDR-ADvisor on a large scale DOE reference commercial building , using actual meteoro-logical year data. We show how the tree based methods can achieve a good predictionaccuracy of 3− 6% on average for all the cases. The biggest advantage of DR-Advisor isthat it completely bypasses the need to build high fidelity models of buildings e.g., withEnergyPlus or with RC networks. Another major advantage of DR-Advisor is that themodels it builds are highly interpretable and simple, an attribute which is often com-pletely neglected in the design of such algorithms. These advantages combines with thefact that the tree based methods can achieve high prediction accuracies, make DR-Advisora very alluring tool for evaluating and planning DR curtailment responses. DR-Advisor(Figure 2) is being developed into a free and open source tool. Since DR-Advisor is adata-driven approach, it can be easily scaled to multiple buildings and can be used forcampus-wide demand response which is a part of our on-going work.REFERENCES1. Melillo, J. M., Richmond, T., and Yohe, G. W.: Climate change impacts in the unitedstates: the third national climate assessment. US Global change research program, 841,2014.2. Goldman, C.: Coordination of energy efficiency and demand response. Lawrence Berke-ley National Laboratory, 2010.3. Commission, F. E. R.: Assessment of demand response and advanced metering. 2008.4. pjm interconnection: 2014 demand response operations markets activity report. 2014.5. Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A.: Classification and regres-sion trees. CRC press, 1984.6. Breiman, L.: Random forests. Machine learning, 45(1):5–32, 2001.7. Deru, M., Field, K., Studer, D., et al.: U.s. department of energy commercial referencebuilding models of the national building stock. 2010.406 CISBAT 2015 - September 9-11, 2015 - Lausanne, Switzerland

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DR-Advisor: A Data Driven Demand Response Recommender System

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