Day-ahead electricity market forecasting using kernels

Vasileios Kekatos, Sriharsha Veeramachaneni, Marc Light, Georgios B Giannakis

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

6 Scopus citations

Abstract

Weather and life cycles, fuel markets, reliability rules, scheduled and random outages, renewables and demand response programs, all constitute pieces of the electricity market puzzle. In such a complex environment, forecasting electricity prices is a very challenging task; nonetheless, it is of paramount importance for market participants and system operators. Day-ahead price forecasting is performed in the present paper using a kernel-based method. This machine learning approach offers unique advantages over existing alternatives, especially in systematically exploiting the spatio-temporal nature of 10-cational marginal prices (LMPs), while nonlinear cause-effect relationships can be captured by carefully selected similarities. Beyond conventional time-series data, non-vectorial attributes (e.g., hour of the day, day of the week, balancing authority) are transparently utilized. The novel approach is tested on real data from the Midwest ISO (MISO) day-ahead electricity market over the summer of 2012, during which MISO's load peak record was observed. The resultant day-ahead LMP forecasts outperform price repetition and ordinary linear regression, thus offering a promising inference tool for the electricity market.

Original languageEnglish (US)
Title of host publication2013 IEEE PES Innovative Smart Grid Technologies Conference, ISGT 2013
DOIs
StatePublished - May 6 2013
Event2013 IEEE PES Innovative Smart Grid Technologies Conference, ISGT 2013 - Washington, DC, United States
Duration: Feb 24 2013Feb 27 2013

Publication series

Name2013 IEEE PES Innovative Smart Grid Technologies Conference, ISGT 2013

Other

Other2013 IEEE PES Innovative Smart Grid Technologies Conference, ISGT 2013
Country/TerritoryUnited States
CityWashington, DC
Period2/24/132/27/13

Keywords

  • Locational marginal prices
  • kriging filtering
  • machine learning
  • wholesale electricity market

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