Optimization based data mining approach for forecasting real-time energy demand

Olu Femi A. Omitaomu, Xueping Li, Shengchao Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution (e.g., 15-minutes interval). While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

Original languageEnglish
Title of host publicationIIE Annual Conference and Expo 2015
PublisherInstitute of Industrial Engineers
Pages536-545
Number of pages10
ISBN (Electronic)9780983762447
StatePublished - 2015
EventIIE Annual Conference and Expo 2015 - Nashville, United States
Duration: May 30 2015Jun 2 2015

Publication series

NameIIE Annual Conference and Expo 2015

Conference

ConferenceIIE Annual Conference and Expo 2015
Country/TerritoryUnited States
CityNashville
Period05/30/1506/2/15

Keywords

  • Data mining
  • Energy management
  • Optimization
  • Process improvement
  • Simulated annealing

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