Short-Term Power Load Forecasting Based on CEEMDAN-WT-VMD Joint Denoising and BiTCN-BiGRU-Attention

Short-term power load forecasting is crucial for safe grid operation. To address the insufficiency of traditional decomposition methods in suppressing high-frequency noise within multi-source noisy time series, this study proposes a hybrid forecasting model integrating CEEMDAN-WT-VMD joint denoising with a BiTCN-BiGRU-Attention architecture. The methodology comprises three stages: (1) CEEMDAN decomposition of raw load data to mitigate mode mixing and extract stationary IMF components; (2) wavelet threshold denoising to filter high-frequency interference while preserving and reconstructing low-frequency signals; (3) secondary feature decomposition using Variational Mode Decomposition (VMD) to enhance data stability. A hybrid architecture combines a Bidirectional Temporal Convolutional Network (BiTCN) for long-term dependency capture, a Bidirectional Gated Recurrent Unit (BiGRU) for dynamic feature extraction, and an attention mechanism for key pattern emphasis. The final load forecasting value is generated by progressively accumulating predictions of decomposed components. Empirical analysis based on power load data from a region in Australia demonstrates that, through horizontal and vertical comparative experiments, the proposed hybrid method demonstrates significant improvements in both forecasting accuracy and stability compared to other frontier hybrid models.