Abstract:

Objectives Load prediction is crucial for power system planning and operation. However, the high proportion of new energy increases source-load uncertainty, leading to greater volatility in the load of new-type power systems, which makes it more difficult to accurately ^{predict multi-user load. To improve the accuracy of short-term load forecasting in new power systems, a multi-user short-term load prediction method based on frequency-domain enhanced Transformer is proposed. Methods The encoder in the Transformer is used to capture the feature information of multi-load sequences. The frequency-domain information is obtained by the discrete cosine transform (DCT), and the channel attention mechanism is used for data enhancement. The decoder in the Transformer is used to integrate the feature information which is then fed into the fully connected layer to obtain the prediction data. To verify the superiority of the proposed method, the Electricity dataset is used for case analysis, and a comparative analysis is conducted between the proposed method and five commonly used load forecasting methods. Results Compared with the traditional Transformer load prediction model, the error of the proposed method is reduced by 21.8%. Conclusions The proposed method demonstrates excellent accuracy and stability, showing feasibility for practical applications.}

Key words: power load forecasting, new energy, source-load uncertainty, deep learning, Transformer model, discrete cosine transform, channel attention mechanism, data enhancement