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Forecasting power demand in China with a CNN-LSTM model including multimodal information

Author

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  • Wang, Delu
  • Gan, Jun
  • Mao, Jinqi
  • Chen, Fan
  • Yu, Lan

Abstract

Accurate forecasting of social power demand is the country's primary task in making decisions on power overall planning, coal power withdrawal, and renewable energy investment. The integration of text data based and traditional time series data may improve the power demand forecasting ability. Therefore, based on the idea of multimodal information fusion, we construct a novel comprehensive power demand prediction model CNN-LSTM (Convolution Neural Network, Long Short-term Memory) in a multi-heterogeneous data environment. Empirical results show that the proposed prediction model is effective, and it proves that the organic fusion of time series data and text data can effectively improve forecasting performance. And China's power demand growth will gradually slow down or even show a downward trend in the next two years, which provides an important decision-making reference for the low-carbon transformation of China's power system.

Suggested Citation

  • Wang, Delu & Gan, Jun & Mao, Jinqi & Chen, Fan & Yu, Lan, 2023. "Forecasting power demand in China with a CNN-LSTM model including multimodal information," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222028985
    DOI: 10.1016/j.energy.2022.126012
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