Empowering Sustainability in Power Grids: A Multi-Scale Adaptive Load Forecasting Framework with Expert Collaboration

Accurate and robust power load forecasting is a cornerstone for efficent energy management and the sustainable integration of renewable energy. However, the practical application of current deep learning methods is hindered by two critical challenges: the rigidity of fixed-length prediction horizons and the difficulty in capturing the complex, heterogeneous temporal patterns found in real-world load data. To address these limitations, this paper proposes the multi-scale adaptive forecasting with multi-expert collaboration (MAFMC) framework. MAFMC’s primary contribution is a novel architecture that utilizes a collaborative ensemble of specialized expert predictors, enabling it to dynamically adapt to complex and non-linear load dynamics with superior accuracy. Furthermore, it introduces an innovative iterative learning strategy that allows for highly flexible, variable-length forecasting without the need for costly and time-consuming retraining. This capability significantly enhances operational efficiency in dynamic energy environments. Extensive evaluations on three benchmark datasets demonstrate that MAFMC achieves state-of-the-art performance, consistently outperforming leading baseline methods and establishing a new standard for power load forecasting.