Local Learning-ARIMA adaptive hybrid architecture for hourly electricity price forecasting

A. Vaccaro, T. H.M. El-Fouly, C. A. Canizares, K. Bhattacharya

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

11 Scopus citations


The paper proposes a hybrid architecture for electricity price forecasting. The proposed architecture combines the advantages of the easy-to-use and relatively easy-to-tune Autoregressive Integrated Moving Average (ARIMA) models and the approximation power of local learning techniques. The architecture is robust and more accurate than the individual forecasting methodologies on which it is based, since it combines a reliable built-in linear model (ARIMA) with an adaptive dynamic corrector (Lazy Learning algorithm). The corrector model is sequentially updated, in order to adapt the whole architecture to varying market conditions. Detailed simulation studies show the effectiveness of the proposed hybrid learning methods for forecasting the volatile Hourly Ontario Energy Prices (HOEPs) of the Ontario, Canada, electricity market.

Original languageBritish English
Title of host publication2015 IEEE Eindhoven PowerTech, PowerTech 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479976935
StatePublished - 31 Aug 2015
EventIEEE Eindhoven PowerTech, PowerTech 2015 - Eindhoven, Netherlands
Duration: 29 Jun 20152 Jul 2015

Publication series

Name2015 IEEE Eindhoven PowerTech, PowerTech 2015


ConferenceIEEE Eindhoven PowerTech, PowerTech 2015


  • adaptive systems
  • Local Learning
  • Prediction models
  • price forecasting


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