A Data-Driven Approach for Targeting Residential Customers for Energy Efficiency Programs

Targeting the right customers for energy efficiency (EE) programs is crucial for the power distribution or retail companies to enhance the efficiency of marketing budget allocation and the yield of energy savings. This work presents a scalable methodology for targeting residential customers for EE programs that focus on reducing unnecessary domestic energy consumption and replacing low efficient refrigerator-freezers by using smart meter data and daily temperature data. A novel method is proposed to detect baseload (i.e., power constantly consumed by some appliances that are never turned off) segments from daily load profiles. Test on ground truth data shows the high accuracy performance of the proposed method and its adaptiveness to the heterogeneity in energy consumptions across customers. Then we discuss how the proposed method can be utilized to identify customers with high baseload energy saving potentials and low efficient refrigerator-freezers. Case studies validate that the proposed targeting strategy far outperforms random selection.

[1]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[2]  Alan Meier.  Refrigerator energy use in the laboratory and in the field , 1995 .

[3]  J. Cavallo,et al.  Targeting refrigerators for repair or replacement. , 2000 .

[4]  Ram Rajagopal,et al.  Household Energy Consumption Segmentation Using Hourly Data , 2014, IEEE Transactions on Smart Grid.

[5]  Brent Griffith,et al.  METHODOLOGY FOR ANALYZING THE TECHNICAL POTENTIAL FOR ENERGY PERFORMANCE IN THE U.S. COMMERCIAL BUILDINGS SECTOR WITH DETAILED ENERGY MODELING , 2006 .

[6]  Silvia Santini,et al.  Revealing Household Characteristics from Smart Meter Data , 2014 .

[7]  B. D. Hunn,et al.  The use of data envelopment analysis for evaluating building energy consumption in terms of productivity , 1998 .

[8]  Zheng Yan,et al.  Estimating wind speed probability distribution by diffusion-based kernel density method , 2015 .

[9]  Ram Rajagopal,et al.  Ranking appliance energy efficiency in households: Utilizing smart meter data and energy efficiency frontiers to estimate and identify the determinants of appliance energy efficiency in residential buildings , 2015 .

[10]  Dirk P. Kroese,et al.  Kernel density estimation via diffusion , 2010, 1011.2602.

[11]  John E. Taylor,et al.  Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions , 2012 .

[12]  Samuel Dalton Borgeson,et al.  Targeted Efficiency: Using Customer Meter Data to Improve Efficiency Program Outcomes , 2013 .

[13]  K. Gillingham,et al.  Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence , 2013, Review of Environmental Economics and Policy.

[14]  R. Boddy,et al.  Statistical Methods in Practice: For Scientists and Technologists , 2009 .

[15]  Margaret F. Fels.  PRISM: An Introduction , 1986 .

[16]  Fredrik Wallin,et al.  Smart meter data clustering using consumption indicators: responsibility factor and consumption variability , 2017 .

[17]  Haji Hassan Masjuki,et al.  Role of ambient temperature, door opening, thermostat setting position and their combined effect on refrigerator-freezer energy consumption , 2002 .

[18]  Jin Wen,et al.  Reducing energy consumption in low income public housing: Interviewing residents about energy behaviors , 2013 .

[19]  Cheri A. Levinson,et al.  How bandwidth selection algorithms impact exploratory data analysis using kernel density estimation. , 2014, Psychological methods.

[20]  Anand Patwardhan,et al.  Big Data and Residential Energy Efficiency Evaluation , 2018 .

[21]  Umberto Berardi,et al.  A data-driven approach for building energy benchmarking using the Lorenz curve , 2018, Energy and Buildings.

[22]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[23]  Muhd Zaimi Abd Majid,et al.  A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries) , 2015 .

[24]  Rainer Stamminger,et al.  Analysis of effecting factors on domestic refrigerators’ energy consumption in use , 2013 .

[25]  Haibo He,et al.  A local density-based approach for outlier detection , 2017, Neurocomputing.

[26]  J. Lemoine,et al.  Electricity demand in european households: major findings from an extensive end-use metering project in 4 individual countries , 2002 .

[27]  Thorsten Staake,et al.  Enhancing energy efficiency in the residential sector with smart meter data analytics , 2018, Electronic Markets.

[28]  Thorsten Staake,et al.  Feature extraction and filtering for household classification based on smart electricity meter data , 2014, Computer Science - Research and Development.