Sensors & Behavior (ARPA-E) > Technology Projects

Energy consumption, segmentation and forecasting

Electric utilities generally market demand-response and energy-efficiency programs to very broad groups.




Example avg daily kWh using hourly data.

Investigators: Ram Rajagopal, Martin Fischer, June A. Flora, Jung Suk Kwac, Adrian Albert, Amir Kavousian, Jeff Wong

Electric utilities generally market demand-response and energy-efficiency programs to very broad groups, such as residential customers and small businesses, without knowing which programs are reducing consumption during periods of very high demand or, in the case of efficiency, eliminating waste. A major promise of smart grid meters is to make such programs more effective, yet using the vast amounts of data from smart meters for this is poorly understood. This project experimented with several methods to process data to inform groups of customers about the most worthwhile efficiency measures to consider, to understand the demand-response potential in particular circumstances, and to help utilities provide energy at minimum cost and with minimum environmental impact.

This project segmented utility customers into groups that exhibit similar energy consumption behaviors, based on a detailed history of energy consumption, plus information about building locations, demographics and weather conditions at the time of consumption. It used data from about 900 Google PowerMeter participants measuring electricity use every 10 minutes and a PG&E sample of approximately 12,000 customers with hourly smart meter data.

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Publications and Presentations

Data-Driven Benchmarking of Building Energy Efficiency Utilizing Statistical Frontier Models
Journal of Computing in Civil Engineering, 28(1), 79-88
Kavousian, A., Rajagopal, R. (2014)

Household Energy Consumption Segmentation Using Hourly Data
IEEE Transactions on Smart Grid, 5(1), 420-430
Kwac, J., Flora, J., Rajagopal, R. (2014)

Determinants of Residential Electricity Consumption: Using Smart Meter Data to Examine the Effect of Climate, Building Characteristics, Appliance Stock, and Occupants' Behavior
Energy, 55, 184-194
Kavousian, A. Rajagopal, R., Fischer, M. (2013)

Smart Meter Driven Segmentation: What Your Consumption Says About You
IEEE Transactions on Power Systems, 28 (4), 4109-4030
Albert, A., Rajagopal, R. (2013)

Utility Customer Segmentation Based on Smart Meter Data: Empirical Study
2013 IEEE International Conference on Smart Grid Communications, 720-725
Kwac, J., Tan, C-W., Sintov, N., Flora, J. Rajagopal, R. (2013)

Building Dynamic Thermal Profiles of Energy Consumption for Individuals and Neighborhoods
2013 IEEE International Conference on Big Data, 723-728
Albert, A., Rajagopal, R. (2013)

A Method to Disaggregate Structural and Behavioral Determinants of Residential Electricity Consumption (680KB PDF)
Presented at Behavior, Energy and Climate Change Conference, 2011
Kavousian, A., Rajagopal, R., Fischer, M. (2011)

Future Work

As part of Phase II of the Energy Sensors & Behavior Initiative, researchers are developing about three types of analytics using smart meter data, which will support a behavioral program(s), and serve as a foundation for the Segmentation and Baseline Analytics Platform. They will also develop data visualization methods used by Stanford projects, partners, etc. They will also improve segmentation and apply the methods to a behavior program project(s), develop base-lining (for calculating accurate energy savings), and segmentation visualizations and reports (to allow others to evaluate their programs with smart meter data). Finally, they will develop and test intervention(s) based on the team's advanced segmentation and information feedback analytics.