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Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm

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  • Wang, Lin
  • Hu, Huanling
  • Ai, Xue-Yi
  • Liu, Hua

Abstract

Electricity energy consumption (EEC) has great effect on the government to make reasonable energy policy and has attracted great attentions of the power generation groups with the liberalization of competition in the electricity industry. In fact, the EEC is easily affected by many factors, including the climate factor and the gross domestic product. So, the precise forecasting of electricity energy consumption is very challenging. This study aims to propose an effective and stable model named ESN-DE using an improved echo state network for forecasting electricity energy consumption. Differential evolution algorithm is used to search optimal values of the three crucial parameters of echo state network. Two comparative examples and an extended example are used to validate the applicability and accuracy of the proposed ESN-DE. Errors of the comparative examples where mean absolute percentage errors of ESN-DE are 1.516% and 0.570% respectively indicate that the ESN-DE outperforms the traditional echo state network and the existing best model. Mean absolute percentage error of ESN-DE is 2.156% for Zhengzhou City's electricity energy consumption forecasting. The proposed ESN-DE is a potential candidate for effective forecasting of electricity energy consumption because of its easy implementation and stability.

Suggested Citation

  • Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
    • Handle: RePEc:eee:energy:v:153:y:2018:i:c:p:801-815
    DOI: 10.1016/j.energy.2018.04.078
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    10. Ghimire, Sujan & Nguyen-Huy, Thong & AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2023. "A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction," Energy, Elsevier, vol. 275(C).
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    12. A-Hyun Jung & Dong-Hyun Lee & Jin-Young Kim & Chang Ki Kim & Hyun-Goo Kim & Yung-Seop Lee, 2022. "Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea," Energies, MDPI, vol. 15(21), pages 1-13, October.
    13. Hasnat Bin Tariq & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Ahmad H. Milyani, 2021. "Maximum-Likelihood-Based Adaptive and Intelligent Computing for Nonlinear System Identification," Mathematics, MDPI, vol. 9(24), pages 1-23, December.
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    17. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
    18. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    19. Hu, Huanling & Wang, Lin & Tao, Rui, 2021. "Wind speed forecasting based on variational mode decomposition and improved echo state network," Renewable Energy, Elsevier, vol. 164(C), pages 729-751.
    20. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
    21. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
    22. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
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