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Smart Meter Data-driven Targeting of Energy Programs


Stanford Reference:

13-166


Abstract


Engineers in Prof. Ram Rajagopal’s laboratory have developed scalable algorithms to identify energy-usage patterns for efficiently targeting energy efficiency (EE) and demand response (DR) programs. This technology utilizes high resolution temporal energy consumption data from smart meters to build customer profiles and analyze the potential for successfully engaging them in EE or DR programs (see usage profile examples below). This data-driven approach takes costs and systems constraints into account to help develop programs tailored for specified lifestyles or to target existing programs to customers for whom they will be most effective.

Flow of data-driven targeting of energy programs

Usage profile examples for segmentation


Applications


  • Energy demand management - data analytics of high resolution temporal energy consumption for:
    • Segmenting customers by lifestyle profile to tailor EE and DR programs
    • DR programs targeting selected customers that are most likely to adequately balance demand

Advantages


  • Data-driven - models rely on high resolution usage data (not demographic variables) to make predictions
  • Accounts for system constraints - optimization formulation includes both costs and network system constraints
  • Scalable - algorithms designed to cope with computation issues coming from large data sets

Publications


  • Flora, June, Jungsuk Kwac, and Ram Rajagopal. "Customer energy consumption segmentation using time-series data." U.S. Patent Publication No.20,150,161,233. 11 Jun. 2015.
  • Kwac, Jungsuk, and Ram Rajagopal. "Data-driven targeting of energy programs using time-series data." U.S. Patent Publication No. 20,150,186,827. 2 Jul. 2015.
  • Jungsuk Kwac; Chin-Woo Tan; Sintov, N.; Flora, J.; Rajagopal, R., "Utility customer segmentation based on smart meter data: Empirical study," 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), 720-5, 21-24 Oct. 2013, Vancouver, BC.
  • Jungsuk Kwac and Ram Rajagopal, "Demand response targeting using big data analytics", 2013 IEEE International Conference on Big Data, 683-690, 6-9 Oct. 2013, Silicon Valley, CA.
  • Jungsuk Kwac, June Flora and Ram Rajagopal, "Energy Lifestyle Segmentation Using Hourly Electricity Data", Behavior Energy and Climate Conference, Nov. 2013, Sacramento, CA.
  • Jungsuk Kwac, June Flora and Ram Rajagopal, "Household Energy Consumption Segmentation using Hourly Data", Smart Grid, IEEE Trans, Vol 5, p420-430, 2014.

Innovators & Portfolio



Date Released

 9/8/2015
 

Licensing Contact


Luis Mejia, Senior Licensing Associate
(650) 725-9409 (Direct)
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Related Keywords


customer selection   data analytics   big data   computer: data mining   computer: temporal data mining   data analysis software   data mining   data mining software   Energy Consumption   energy demand management   energy metering   energy monitoring   smart grid   Smart homes   smart meter data   demand-response user selection   energy management   
 

   

  

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