Abstract: Abstract: Due to the volatile and complex nonlinear characteristics of electricity prices， the prediction accuracy of existing models is often inadequate. To improve prediction accuracy and dig deeper for valuable information within complex nonlinear characteristics， a hybrid model for electricity price prediction based on DWT-SCINet-MDSC is proposed. Firstly， Discrete Wavelet Transform （DWT） is employed by the model to decompose the data into sub-signals at different time scales. This process not only effectively filtered out high-frequency noise but also more significantly reduced data volatility， thereby enhancing the signal-to-noise ratio and rendering the data clearer and more stable. Secondly， multi-scale separable convolutions are utilized to capture rich information across different time scales while effectively minimizing the number of model parameters， thus accelerating the training process. Lastly， to overcome the limitations of manual feature engineering， a feature weighting module is incorporated to adjust the weights of key features， assigning greater importance to critical features for more efficient feature extraction. A simulation experiment was conducted on an electricity price dataset from a region in Australia. The results indicate that， compared with SCINet and other comparative models， the average absolute error is reduced by 23.29%. This demonstrates that the prediction performance of the DWT-SCINet-MDSC hybrid model is significantly improved.

Key words: Key words: deep learning, electricity price prediction, sample convolution interaction, depth separable convolution, discrete wavelet transform