Probabilistic baseline estimation via Gaussian process

Yang Weng, Ram Rajagopal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Scopus citations


Demand response aims at utilizing flexible loads to operate power systems in an economically efficient way. A fundamental question in demand response is how to conduct a baseline estimation to deal with increasing uncertainties in power systems. Unfortunately, traditional baseline estimation lacks the ability to characterize uncertainties due to their deterministic modeling. This deficiency often results in erroneous system operations and miscalculated payments that discourage participating customers. In this paper, we propose a Gaussian process-based approach to mitigate the problem. It features the ability to use all historical data as a prior knowledge, and adjust the estimation according to similar daily patterns in the past. To characterize the uncertainties, this method provides a probabilistic estimate that can be used to not only increase estimation confidence for system operators but also to fairer treatment to customers. Finally, simulation results from Pacific Gas and Electric Company data show that this new method can produce a highly accurate estimate, which dramatically reduces the uncertainties inherent in the distribution power grid. Such a work opens the door for power system operation based on probabilistic estimate.

Original languageEnglish (US)
Title of host publication2015 IEEE Power and Energy Society General Meeting, PESGM 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781467380409
StatePublished - Sep 30 2015
Externally publishedYes
EventIEEE Power and Energy Society General Meeting, PESGM 2015 - Denver, United States
Duration: Jul 26 2015Jul 30 2015


OtherIEEE Power and Energy Society General Meeting, PESGM 2015
Country/TerritoryUnited States


  • aggregation
  • Baseline estimation
  • demand response
  • Gaussian process
  • machine learning
  • probabilistic estimation

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering


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