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Deep Learning for Forecasting Electricity Demand in Taiwan

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  • Cheng-Hong Yang

    (Department of Business Administration, Tainan University of Technology, Tainan 71002, Taiwan
    Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
    Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
    School of Dentistry, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

  • Bo-Hong Chen

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Chih-Hsien Wu

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Kuo-Chang Chen

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Li-Yeh Chuang

    (Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84001, Taiwan)

Abstract

According to the World Energy Investment 2018 report, the global annual investment in renewable energy exceeded USD 200 billion for eight consecutive years until 2017. In this paper, a deep-learning-based time-series prediction method, namely a gated recurrent unit (GRU)-based prediction method, is proposed to predict energy generation in Taiwan. Data on thermal power (coal, oil, and gas power), renewable energy (conventional hydropower, solar power, and wind power), pumped hydropower, and nuclear power generation for 1991 to 2020 were obtained from the Bureau of Energy, Ministry of Economic Affairs, Taiwan, and the Taiwan Power Company. The proposed GRU-based method was compared with six common forecasting methods: autoregressive integrated moving average, exponential smoothing (ETS), Holt–Winters ETS, support vector regression (SVR), whale-optimization-algorithm-based SVR, and long short-term memory. Among the methods compared, the proposed method had the lowest mean absolute percentage error and root mean square error and thus the highest accuracy. Government agencies and power companies in Taiwan can use the predictions of accurate energy forecasting models as references to formulate energy policies and design plans for the development of alternative energy sources.

Suggested Citation

  • Cheng-Hong Yang & Bo-Hong Chen & Chih-Hsien Wu & Kuo-Chang Chen & Li-Yeh Chuang, 2022. "Deep Learning for Forecasting Electricity Demand in Taiwan," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2547-:d:868621
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    References listed on IDEAS

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    Cited by:

    1. Sandipa Bhattacharya & Mitali Sarkar & Biswajit Sarkar & Lakshmi Thangavelu, 2023. "Exploring Sustainability and Economic Growth through Generation of Renewable Energy with Respect to the Dynamical Environment," Mathematics, MDPI, vol. 11(19), pages 1-22, September.
    2. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.

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