Joint load prediction of multiple buildings using multi-task learning with selected-shared-private mechanism

Abstract

Short-term load forecasting for residential buildings is of great significance in ensuring the safe and economic operation of the power grid. However, most existing prediction methods focus solely on the temporal characteristics of individual buildings and ignore the spatial correlation between different buildings in a neighborhood. To tackle this problem and realize joint prediction for multiple buildings, a novel multi-task learning model with a selected-shared-private mechanism is proposed in this paper. Firstly, the temporal and spatial characteristics hidden in electricity consumption patterns are analyzed and represented by auto-correlation and cross-correlation, respectively. Then, a correlation-oriented combination strategy is proposed to build input feature set for the prediction model, and the temporal convolutional network is adopted to extract features. Furthermore, a novel selected-shared-private mechanism is designed for multi-task learning to improve the prediction accuracy, which can selectively utilize information from related tasks while learning private features. The proposed model is compared with other methods on the public dataset, and the results demonstrate that the proposed model can make satisfactory joint predictions for multiple buildings with more than 35% accuracy improvement over the support vector machine.