Accurate load forecasting is crucial for the stable operation and efficient dispatch of power grids. However, directly uploading raw load data to the cloud poses privacy risks, while the complexity of multifrequency, non-stationary signals and the challenges in multiscale feature extraction and hyperparameter optimization hinder traditional forecasting methods. To address these issues, this article proposes a cloud-edge collaborative deep learning framework for power load forecasting, termed edge-cloud collaborative strategy (EC-CVNT). On the edge side, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) are integrated to extract multiscale features and denoise the raw data, thereby reducing computational burden on the cloud and enhancing data privacy. The processed features are then transmitted to the cloud, where the Transformer model is optimized using the Newton-Raphson-based optimizer (NRBO) to improve forecasting accuracy under complex conditions. Validation experiments conducted on a microgrid in Yantai, Shandong Province, China, demonstrate that the integration of CEEMDAN and VMD significantly increases predictive accuracy. Comparative experiments with various hybrid models further verify that EC-CVNT offers substantial advantages in enhancing power load forecasting performance.
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