一种利用多任务学习的短期住宅负荷预测方案

In smart grid regarded as specific embodying of cyber-physical-social system, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly role in planning and operation of smart power system. Considering the similarity of electricity consumption between users, inspired by multi-task learning, the article puts forward an effective residential load forecasting based on multi-task learning model. In detail, the K-means clustering technology and Pearson correlation coefficient are used to select two similar users. Then these two user's load data are merged as input, the bidirectional long short-term memory network is used as a sharing layer to fully capture the relationship between the data of the two users, and then two fully-connection task-specific output layers are respectively built. Based on real datasets, the proposed scheme is thoroughly compared with several typical deep learning based load forecasting schemes. Experiments show that proposed multi-task learning scheme improves the prediction accuracy compared with the existing deep learning prediction scheme.