Short-term load forecasting (STLF) serves as a fundamental basis for the efficient operation of smart grids and energy management systems. Accurate nodal-level load prediction plays a vital role in optimizing power dispatching, reducing operational costs, and enhancing grid security. Variations in user behavior patterns, geographical location, and equipment characteristics among different electricity nodes typically result in load profiles that exhibit pronounced volatility and non-stationarity. Traditional single model forecasting approaches are highly sensitive to data distribution and often struggle to maintain consistently high predictive accuracy across all nodes. Specifically, individual models frequently lack the generalization capability required for diverse load types, leading to significant error variability when applied to nodes with contrasting properties. To address these limitations, this study proposes a highly robust heterogeneous ensemble learning framework. First, a forecasting repository comprising six differentiated base models is constructed to accommodate diversity in model errors. Subsequently, a stacking-based meta-learning strategy is applied to integrate the outputs of the base models, enabling the extraction of multi-dimensional temporal features. To comprehensively validate the effectiveness of the proposed method, extensive comparative experiments are conducted using datasets from ten electricity nodes with diverse distribution characteristics. Experimental results demonstrate that the ensemble model significantly outperforms the base models in terms of accuracy while exhibiting superior stability across various nodes.
Peng et al. (Sat,) studied this question.