Short-Term Electric Power Load Forecasting Based on ISAO-VMD-BiTCN

Abstract

Aiming to improve short-term power load forecasting under nonlinear and nonstationary load characteristics, this paper develops an Improved Snow Ablation Optimizer (ISAO) and applies it to the hyperparameter optimization of a Variational Mode Decomposition (VMD)- Bidirectional Temporal Convolutional Network (BiTCN) forecasting framework. First, VMD is used to decompose the original load sequence into multiple intrinsic mode function (IMF) components in order to reduce the non-stationarity of the signal. Second, to address the limitations of the Snow Ablation Optimizer (SAO), four strategies—quantum-chaotic hybrid initialization, elite-pool-guided dual-population updating, the quantum tunneling effect, and dynamic lens opposition-based learning—are introduced to form the ISAO algorithm. Finally, ISAO is used to independently optimize the BiTCN hyperparameters of each IMF component, and the prediction results of all components are superimposed and reconstructed to obtain the final forecast. CEC2022 benchmark function tests and experiments on actual load data show that the ISAO algorithm achieves superior optimization performance; compared with the SAO-VMD-BiTCN model, the RMSE of the proposed model decreases from 2342.23 MW to 2202.14 MW, a reduction of 5.98% and the proposed model outperforms comparison models such as VMD-BiTCN and PSO-VMD-BiTCN across the RMSE, MAE, MAPE, and R2 metrics. Furthermore, Diebold–Mariano (DM) test results provide additional statistical evidence for the favorable forecasting performance of the proposed framework across multiple random initializations.