Go top
Paper information

Rethinking weather station selection for electric load forecasting using genetic algorithms

S. Moreno, E.F. Sánchez-Úbeda, A. Muñoz

International Journal of Forecasting Vol. 36, nº. 2, pp. 695 - 712

Demand forecasting is and has been for years a topic of great interest in the electricity sector, being the temperature one of its major drivers. Indeed, one of the challenges when modelling the load is to choose the right weather station, or set of stations, for a given load time series. However, only a few research papers have been devoted to this topic. This paper reviews the most relevant methods that were applied during the Global Energy Forecasting Competition of 2014 (GEFCom2014) and presents a new approach to weather station selection, based on Genetic Algorithms (GA), which allows finding the best set of stations for any demand forecasting model, and outperforms the results of existing methods. Furthermore its performance has also been tested using GEFCom2012 data, providing significant error improvements. Finally, the possibility of combining the weather stations selected by the proposed GA using the BFGS algorithm is briefly tested, providing promising results.

Keywords: Electric load forecasting; Energy demand; Weather station selection; Genetic algorithms; Cross-validation; Weather station combination; BFGS algorithm

JCR Impact Factor and WoS quartile: 3,779 - Q1 (2020); 7,900 - Q1 (2022)

DOI reference: DOI icon https://doi.org/10.1016/j.ijforecast.2019.08.008

Published on paper: June 2020.

Published on-line: December 2019.

S. Moreno, E.F. Sánchez-Úbeda, A. Muñoz, Rethinking weather station selection for electric load forecasting using genetic algorithms. International Journal of Forecasting. Vol. 36, nº. 2, pp. 695 - 712, June 2020. [Online: December 2019]

    Research topics:
  • Energy data analytics

pdf Preview
Request Request the document to be emailed to you.