Improving forecast accuracy for grid demand and renewables supply with pattern-match features

Usman S. Sanusi, David Corne

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


Short term forecasts of electricity demand and renewables supply are vital for smart grid applications, and in particular for the successful integration of renewables. Improved forecasting of these quantities supports several aspects of smart grid control and deployment, including planning, pricing, staffing, and more. Globally, there is a target for renewables engagement of ∼36% (percentage of renewables in the global energy mix) by 2030. This is expected to increase global GDP by 1.1%, and to substantially reduce GHG emissions. It is well known that one of the main barriers to the integration of renewables is the relatively unpredictable nature of renewables availability. A further difficulty (in many grid applications, irrespective of renewables), is the similarly unpredictable nature of energy demand. In this paper we explore an idea in the area of time series prediction that was developed specifically for grid-relevant applications, and we test this method on wind-speed data and on energy demand data. The basis of the approach is the exploitation of patterns in the time series that are maximally similar to the current context. However, unlike previous work that has explored the use of such pattern-matching directly for prediction, we instead use the outcome of pattern matching to generate one or more additional features for the subsequent machine learning. Experiments suggest that (i) the approach reliably leads to improved forecasts; and (ii) the approach has wide applicability, independent of the machine learning approach in use.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
ISBN (Electronic)9781509042401
Publication statusPublished - 13 Feb 2017
Event2016 IEEE Symposium Series on Computational Intelligence - Athens, Greece
Duration: 6 Dec 20169 Dec 2016


Conference2016 IEEE Symposium Series on Computational Intelligence
Abbreviated titleSSCI 2016


  • feature engineering
  • feature selection
  • forecasting
  • pattern matching
  • prediction
  • regression
  • renewables
  • time series

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Optimization
  • Artificial Intelligence


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