Forecasting electricity prices in the spot market utilizing wavelet packet decomposition integrated with a hybrid deep neural network

Accurate forecasting of electricity spot prices is crucial for market participants in formulating bidding strategies. However, the extreme volatility of electricity spot prices, influenced by various factors, poses significant challenges for forecasting. To address the data uncertainty of electricity prices and effectively mitigate gradient issues, overfitting, and computational challenges associated with using a single model during forecasting, this paper proposes a framework for forecasting spot market electricity prices by integrating wavelet packet decomposition (WPD) with a hybrid deep neural network. By ensuring accurate data decomposition, the WPD algorithm aids in detecting fluctuating patterns and isolating random noise. The hybrid model integrates temporal convolutional networks (TCN) and long short-term memory (LSTM) networks to enhance feature extraction and improve forecasting performance. Compared to other techniques, it significantly reduces average errors, decreasing mean absolute error (MAE) by 27.3%, root mean square error (RMSE) by 66.9%, and mean absolute percentage error (MAPE) by 22.8%. This framework effectively captures the intricate fluctuations present in the time series, resulting in more accurate and reliable predictions.