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Forecasting energy consumption and wind power generation using deep echo state network

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  • Hu, Huanling
  • Wang, Lin
  • Lv, Sheng-Xiang

Abstract

Accurate energy forecasting is of great significance for the energy sector to formulate short-term plans and long-term development strategies for meeting energy needs. This study develops a stacked hierarchy of reservoirs (DeepESN) for forecasting energy consumption and wind power generation by introducing the deep learning framework into the basic echo state network. DeepESN combines the powerful nonlinear time series modeling ability of echo state network and the efficient learning ability of the deep learning framework. Two comparative examples and an extended application are analyzed to validate the accuracy and reliability of DeepESN. These comparative examples reveal that DeepESN outperforms the existing popular models, persistence model, back-propagation neural network, and echo state network. Moreover, compared with echo state network, DeepESN shows 51.56%, 51.53%, and 35.43% improvements in terms of mean absolute error, root mean square error, and mean absolute percentage error in the extended application, respectively. Therefore, DeepESN is an appropriate tool for forecasting energy consumption and wind power generation on account of its effective and stable forecasting performance.

Suggested Citation

  • Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    • Handle: RePEc:eee:renene:v:154:y:2020:i:c:p:598-613
    DOI: 10.1016/j.renene.2020.03.042
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    5. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    6. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting," Energy, Elsevier, vol. 216(C).
    7. Fath U Min Ullah & Noman Khan & Tanveer Hussain & Mi Young Lee & Sung Wook Baik, 2021. "Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework," Mathematics, MDPI, vol. 9(6), pages 1-22, March.
    8. Yijue Sun & Fenglin Zhang, 2022. "Grey Multivariable Prediction Model of Energy Consumption with Different Fractional Orders," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    9. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    10. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    11. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    12. Hu, Wenyu & E, Jiaqiang & Tan, Yan & Zhang, Feng & Liao, Gaoliang, 2022. "Modified wind energy collection devices for harvesting convective wind energy from cars and trucks moving in the highway," Energy, Elsevier, vol. 247(C).
    13. Xing, Zhikai & He, Yigang, 2023. "Multi-modal multi-step wind power forecasting based on stacking deep learning model," Renewable Energy, Elsevier, vol. 215(C).
    14. 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.
    15. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    16. Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).
    17. Aina Maimó-Far & Alexis Tantet & Víctor Homar & Philippe Drobinski, 2020. "Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain," Energies, MDPI, vol. 13(19), pages 1-25, October.
    18. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
    19. Hu, Huanling & Wang, Lin & Zhang, Dabin & Ling, Liwen, 2023. "Rolling decomposition method in fusion with echo state network for wind speed forecasting," Renewable Energy, Elsevier, vol. 216(C).
    20. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
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    22. Jing Wan & Jiehui Huang & Zhiyuan Liao & Chunquan Li & Peter X. Liu, 2022. "A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting," Mathematics, MDPI, vol. 10(11), pages 1-20, May.
    23. Gao, Ruobin & Li, Ruilin & Hu, Minghui & Suganthan, Ponnuthurai Nagaratnam & Yuen, Kum Fai, 2023. "Dynamic ensemble deep echo state network for significant wave height forecasting," Applied Energy, Elsevier, vol. 329(C).

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