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An adaptive hybrid model for short term electricity price forecasting

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  • Zhang, Jinliang
  • Tan, Zhongfu
  • Wei, Yiming

Abstract

With the large-scale renewable energy integration into the power grid, the features of electricity price has become more complex, which makes the existing models hard to obtain a satisfactory results. Hence, more accurate and stable forecasting models need to be developed. In this paper, a new adaptive hybrid model based on variational mode decomposition (VMD), self-adaptive particle swarm optimization (SAPSO), seasonal autoregressive integrated moving average (SARIMA) and deep belief network (DBN) is proposed for short term electricity price forecasting. The effectiveness of the proposed model is verified by using data from Australian, Pennsylvania-New Jersey-Maryland (PJM) and Spanish electricity markets. Empirical results show that the proposed model can significantly improve the forecasting accuracy and stability.

Suggested Citation

  • Zhang, Jinliang & Tan, Zhongfu & Wei, Yiming, 2020. "An adaptive hybrid model for short term electricity price forecasting," Applied Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:appene:v:258:y:2020:i:c:s030626191931774x
    DOI: 10.1016/j.apenergy.2019.114087
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    References listed on IDEAS

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