Online short-term multi-user load forecasting based on dynamic recognition of spatiotemporal dependencies
Fangzhou Shao
To fully and effectively exploit the spatiotemporal correlations among multi-user loads, this paper proposes an online short-term multi-user load forecasting method based on dynamic recognition of spatiotemporal dependencies. A hybrid graph convolutional network-bidirectional long short-term memory (GCN-BiLSTM) model is employed to capture the complex spatiotemporal relationships among multi-user loads. To enable dynamic recognition of spatiotemporal dependencies, a novel graph distance metric i