A Virtual Power Plant Load Forecasting Approach Using COM Encoding and BiLSTM-Att-KAN

Virtual Power Plant (VPP) is capable of aggregating and intelligently coordinating diverse distributed energy resources, among which the accuracy of load forecasting is a key factor in ensuring their regulation capability. To address the periodicity and complex nonlinear fluctuations of electricity load data, this study introduces a Cyclic Order Mapping (COM) encoding method, which maps weekly and intraday sequences into continuous ordered variables on the unit circle, thereby effectively preserving load periodic features. On the basis of the COM encoding, a novel forecasting model is proposed by integrating Bidirectional Long Short-Term Memory (BiLSTM) networks, an efficient self-attention mechanism, and the Kolmogorov–Arnold Network (KAN). This model is termed BiLSTM-Att-KAN. Comparative and ablation experiments were conducted to assess the scientific validity and predictive accuracy of the proposed approach. The results confirm its superiority, achieving a Root Mean Square Error (RMSE) of 141.403, a Mean Absolute Error (MAE) of 106.687, and a coefficient of determination (R2) of 0.962. These findings demonstrate the effectiveness of the proposed model in enhancing load forecasting performance for VPP applications.