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.
Among the findings:
- A population of about 1,000 users can be segmented into about 10 groups for demand response, and a scalable clustering technique that uses a statistically meaningful distributional metric was developed.
- Using hourly electricity and weather readings to characterize residential customers' temperature-dependent consumption such as air conditioning or heating, segmentation and targeting of users may offer savings twice that of current demand-response programs.
- About 900 households users may be described with good conï¬dence by 13 patterns, with certain types of appliances and behaviors related to appliance operation affecting consumption patterns most. Consumption statistics may be used to target residential energy efï¬ciency programs to achieve greatest impact in curtailing cost of service.
- Inferring occupancy states from consumption time series data showed that temporal patterns in the user's consumption data can predict user consumption at a population level.
- The number of refrigerators and entertainment devices (e.g., VCRs) are among the most important determinants of daily minimum consumption, while the number of occupants and high-consumption appliances such as electric water heaters are the most significant determinants of daily maximum consumption.
- Acknowledging climate change as a motivation to save energy showed correlation with lower electricity consumption.
- Contrary to some previous studies, the researchers observed no significant correlation between electricity consumption and income level, home ownership, or building age.
- Some otherwise energy-efficient features such as energy-efficient appliances, programmable thermostats and insulation were correlated with a slight increase in electricity consumption.
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)
As part of Phase II of the Stanford Energy 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.