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

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Michał Sabat & Dariusz Baczyński, 2021. "Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case," Energies, MDPI, vol. 14(11), pages 1-19, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3204-:d:565753
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    References listed on IDEAS

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    1. Huang, Jianhua & Gurney, Kevin Robert, 2016. "The variation of climate change impact on building energy consumption to building type and spatiotemporal scale," Energy, Elsevier, vol. 111(C), pages 137-153.
    2. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    3. Jianhua Huang & Kevin Robert Gurney, 2016. "Impact of climate change on U.S. building energy demand: sensitivity to spatiotemporal scales, balance point temperature, and population distribution," Climatic Change, Springer, vol. 137(1), pages 171-185, July.
    4. Gangjun Gong & Xiaonan An & Nawaraj Kumar Mahato & Shuyan Sun & Si Chen & Yafeng Wen, 2019. "Research on Short-Term Load Prediction Based on Seq2seq Model," Energies, MDPI, vol. 12(16), pages 1-18, August.
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