Efficient Feature Selection Strategy for Accurate Electricity Demand Forecasting

Abinet Tesfaye Eseye, Matti Lehtonen, Toni Tukia, Semen Uimonen, Robert Millar

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

1 Citation (Scopus)
119 Downloads (Pure)


Accurate electric load prediction offers an important input information for various smart decisions in energy systems. This paper focuses on short-term forecasting of the electric load of small-scale decentralized energy systems (buildings, energy communities, microgrids, virtual power plants, local energy internets, etc.), which are newly evolving energy system models. Quite few researchers have done feature selection before training forecast models, which is an important preprocessing task of data mining and broadly practiced for knowledge exploration in expert and intelligent systems. This paper proposes a feature selection strategy to find the most significant and non-repetitive variables for accurate short-term electric load prediction in distributed energy systems in general and buildings in particular. In the devised strategy, Binary Genetic Algorithm (BGA) is employed for the variable selection task and Gaussian Process Regression (GPR) is applied for quantifying the fitness score of the variables. The proposed feature selection strategy is implemented and validated using actual electricity consumption data of various buildings located in Otaniemi area of Espoo, Finland. The results are compared with those obtained by other predictor selection methods and show outperformed performances.
Original languageEnglish
Title of host publicationProceedings of the IEEE PES Europe Conference on Innovative Smart Grid Technologies, ISGT-Europe 2019
Number of pages5
ISBN (Electronic)978-1-5386-8218-0
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventIEEE PES Europe Conference on Innovative Smart Grid Technologies - University POLITEHNICA of Bucharest, Bucharest, Romania
Duration: 29 Sep 20192 Oct 2019

Publication series

NameIEEE PES Innovative Smart Grid Technologies Conference Europe
ISSN (Print)2165-4816
ISSN (Electronic)2165-4824


ConferenceIEEE PES Europe Conference on Innovative Smart Grid Technologies
Abbreviated titleISGT-Europe
City Bucharest
Internet address


  • BGA
  • Electricity demand forecasting
  • Feature selection
  • Fitness evaluation measure
  • GPR
  • Smart grid


Dive into the research topics of 'Efficient Feature Selection Strategy for Accurate Electricity Demand Forecasting'. Together they form a unique fingerprint.

Cite this