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A developed hybrid forecasting system for energy consumption structure forecasting based on fuzzy time series and information granularity

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  • Jiang, Ping
  • Yang, Hufang
  • Li, Hongmin
  • Wang, Ying

Abstract

The energy consumption structure has a crucial influence on the sustainable development of the economy and on the environment, and it has drawn the attention of scholars and managers. The forecasting of different types of energy consumption, especially small-sample forecasting, has been a challenging task because of the limitation of the sample size. Thus, in this study, a novel forecasting system based on fuzzy time series that is appropriate for small-sample forecasting was developed. Specifically, the fuzzy time series, which deals with the fuzzy set, is applied as the forecasting program. In fuzzy time series forecasting, the information granularity and fuzzy c-means clustering are utilized for fuzzification. Moreover, an improved chaotic electromagnetic field optimization algorithm is applied to search for the optimal parameters of the information granularity. The experiments and comparison verified that the proposed forecasting system has an excellent performance in energy consumption forecasting with great accuracy and stability, providing accurate forecasting for the energy consumption structure.

Suggested Citation

  • Jiang, Ping & Yang, Hufang & Li, Hongmin & Wang, Ying, 2021. "A developed hybrid forecasting system for energy consumption structure forecasting based on fuzzy time series and information granularity," Energy, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:energy:v:219:y:2021:i:c:s0360544220327067
    DOI: 10.1016/j.energy.2020.119599
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    2. Pang, Qinghua & Dong, Xianwei & Zhang, Lina & Chiu, Yung-ho, 2023. "Drivers and key pathways of the household energy consumption in the Yangtze river economic belt," Energy, Elsevier, vol. 262(PA).
    3. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    4. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
    5. Song, Xiang & Wang, Dingyu & Zhang, Xuantao & He, Yuan & Wang, Yong, 2022. "A comparison of the operation of China's carbon trading market and energy market and their spillover effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Hu, Haisheng & Zhao, Laijun & Dong, Wanhao, 2023. "How to achieve the goal of carbon peaking by the energy policy? A simulation using the DCGE model for the case of Shanghai, China," Energy, Elsevier, vol. 278(PA).
    7. Xiyang Yang & Shiqing Zhang & Xinjun Zhang & Fusheng Yu, 2022. "Polynomial Fuzzy Information Granule-Based Time Series Prediction," Mathematics, MDPI, vol. 10(23), pages 1-21, November.
    8. Wang, Chen & Zhang, Shenghui & Liao, Peng & Fu, Tonglin, 2022. "Wind speed forecasting based on hybrid model with model selection and wind energy conversion," Renewable Energy, Elsevier, vol. 196(C), pages 763-781.

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