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Electricity price forecasting using hybrid deep learned networks

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  • Krishna Prakash N.
  • Jai Govind Singh

Abstract

This paper presents a novel hybrid model integrating maximal overlap discrete wavelet transform (MODWT) denoising and empirical mode decomposition (EMD) with sequence‐to‐sequence (seq2seq) long short‐term memory (LSTM) neural networks for day‐ahead electricity price forecasting. The nonstationary and nonlinear time series electricity price data are first denoised using MODWT. The resulting signal is decomposed into several intrinsic mode functions (IMF) with different resolutions by EMD. The extracted IMF is then introduced into seq2seq LSTM to obtain an aggregated predicted value for electricity price. The proposed method is examined using the Nord pool Elspot energy market data. Empirical results show that the proposed model outperformed the other forecasting models like LSTM and stacked LSTM. The performance measures indicate that data denoising can significantly improve the prediction stability and the generalization ability of the LSTM model.

Suggested Citation

  • Krishna Prakash N. & Jai Govind Singh, 2023. "Electricity price forecasting using hybrid deep learned networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1750-1771, November.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:7:p:1750-1771
    DOI: 10.1002/for.2981
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    References listed on IDEAS

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