Short-term load forecasting for an industrial building based on diverse load patterns

Short-term load forecasting for buildings is crucial for effective demand management. Traditional approaches that treat data uniformly for training and testing often fail to account for internal pattern shifts, potentially compromising predictive accuracy. This paper proposes a novel load forecasting method for industrial buildings, incorporating a dynamic weighting strategy that acknowledges these internal data patterns. We introduce an adaptive hybrid clustering algorithm that integrates ordering points with K nearest neighbors. This algorithm categorizes daily load curves into distinct patterns, training specialized models for each category. For each target day in the test set, the K nearest neighbor algorithm determines the appropriate clustering category. To account for variations in load data, we construct separate input datasets based on temporal features and clustering types, which are then used for model training. Finally, dynamic weights, informed by historical error metrics, optimize the integration of these two data construction methods, enhancing the precision of the predictions. Experimental validation in an industrial setting demonstrates the superiority of our method, which improves the mean absolute scaled error (MASE) by 7.94% and 0.88%, respectively, compared to methods that solely rely on temporal features or cluster types.