Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Michał Sabat and Dariusz Baczyński
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Michał Sabat: Electrical Power Engineering Institute, Warsaw University of Technology, Building of Mechanics, ul. Koszykowa 75, 00-662 Warszawa, Poland
Dariusz Baczyński: Electrical Power Engineering Institute, Warsaw University of Technology, Building of Mechanics, ul. Koszykowa 75, 00-662 Warszawa, Poland

Energies, 2021, vol. 14, issue 11, 1-19

Abstract: Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

Keywords: electric energy demand; Pareto fronts in forecasting; K nearest neighbors (kNN) algorithm; nondominated solutions; power engineering challenges (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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