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Comparison and Explanation of Forecasting Algorithms for Energy Time Series

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  • Yuyi Zhang

    (Faculty of Applied Mathematics and Control Processes, Saint-Petersburg State University, Universitetskii Prospekt 35, 198504 St. Petersburg, Russia)

  • Ruimin Ma

    (Faculty of Applied Mathematics and Control Processes, Saint-Petersburg State University, Universitetskii Prospekt 35, 198504 St. Petersburg, Russia)

  • Jing Liu

    (Faculty of Applied Mathematics and Control Processes, Saint-Petersburg State University, Universitetskii Prospekt 35, 198504 St. Petersburg, Russia)

  • Xiuxiu Liu

    (Faculty of Applied Mathematics and Control Processes, Saint-Petersburg State University, Universitetskii Prospekt 35, 198504 St. Petersburg, Russia)

  • Ovanes Petrosian

    (Faculty of Computer Science and Technology, Saint Petersburg Electrotechnical University “LETI”, Professora Popova 5, 197376 St. Petersburg, Russia)

  • Kirill Krinkin

    (Faculty of Computer Science and Technology, Saint Petersburg Electrotechnical University “LETI”, Professora Popova 5, 197376 St. Petersburg, Russia)

Abstract

In this work, energy time series forecasting competitions from the Schneider Company, the Kaggle Online platform, and the American society ASHRAE were considered. These competitions include power generation and building energy consumption forecasts. The datasets used in these competitions are based on reliable and real sensor records. In addition, exogenous variables are accurately added to the dataset. All of these ensure the richness of the information contained in the dataset, which is crucial for energy management. Therefore, (1) We choose to study forecast models suitable for energy management on these energy datasets; (2) Forecast models including popular algorithm structures such as neural network models and ensemble models. In addition, as an innovation, we introduce the Explainable AI method (SHAP) to explain models with excellent performance indicators, thereby strengthening its trust and transparency; (3) The results show that the performance of the integrated model in these competitions is more stable and efficient, and in the integrated model, the advantages of LightGBM are more obvious; (4) Through the interpretation of SHAP, we found that the lagging characteristics of the building area and target variables are important features.

Suggested Citation

  • Yuyi Zhang & Ruimin Ma & Jing Liu & Xiuxiu Liu & Ovanes Petrosian & Kirill Krinkin, 2021. "Comparison and Explanation of Forecasting Algorithms for Energy Time Series," Mathematics, MDPI, vol. 9(21), pages 1-12, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2794-:d:671887
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    References listed on IDEAS

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    2. Inoue, Atsushi & Kilian, Lutz, 2008. "How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 511-522, June.
    3. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
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    1. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.

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