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A Novel Deep Learning Ensemble Model with Secondary Decomposition for Short-Term Electricity Price Forecasting

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Proceedings of 2020 Chinese Intelligent Systems Conference (CISC 2020)

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Abstract

Accurate electricity price forecasting plays a crucial role in the operation and development of the electricity market. In this paper, a novel hybrid model based on hybrid mode decomposition (HMD), convolutional long short term memory network (CNNLSTM), Elman neural network, and Bayesian optimization (BO) is proposed to forecast the electricity price. HMD is used to deeply decompose data into several subsequences, which consists of complete ensemble empirical mode decomposition with adaptive noise, sample entropy and empirical wavelet transform. CNNLSTM and Elman are adopted to forecast the subsequences. Besides, BO is introduced to optimize parameters. Finally, two case studies are taken to justify the effectiveness of the proposed forecasting model. The results show that the proposed model can possess significantly superior forecasting performance.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (No. 61573095). This work is supported by the Natural Science Foundation of Shanghai under grant no. 20ZR1402800.

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Authors and Affiliations

  1. College of Information Sciences and Technology, Donghua University, Shanghai, 201620, China

    Na Chen, Yiming Gan, Wuneng Zhou & Hangyang Cheng

  2. Educational Technology Center, Donghua University, Shanghai, 200051, China

    Xueqing Yang

  3. Guangdong Polytechnic College, Foshan, 528041, China

    Yiming Gan

  4. Engineering Research Center of Digitized Textile and Fashion Technology, Donghua University, Shanghai, China

    Wuneng Zhou

Authors
  1. Na Chen
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  2. Xueqing Yang
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  3. Yiming Gan
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  4. Wuneng Zhou
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  5. Hangyang Cheng
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Corresponding authors

Correspondence to Xueqing Yang or Yiming Gan .

Editor information

Editors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, Beijing, China

    Yingmin Jia

  2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, Beijing, China

    Weicun Zhang

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, Beijing, China

    Yongling Fu

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Chen, N., Yang, X., Gan, Y., Zhou, W., Cheng, H. (2021). A Novel Deep Learning Ensemble Model with Secondary Decomposition for Short-Term Electricity Price Forecasting. In: Jia, Y., Zhang, W., Fu, Y. (eds) Proceedings of 2020 Chinese Intelligent Systems Conference. CISC 2020. Lecture Notes in Electrical Engineering, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-15-8450-3_8

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