STGNet: Short-term residential load forecasting with spatial-temporal gated fusion network

With the continuous transformation of the global power system, ensuring the flexibility and stability of the power system has become a direction that the global energy industry has been striving for. This puts forward higher requirements for load forecasting, especially high-precision short-term residential load forecasting. However, the high volatility and uncertainty of electric consumption make this problem challenging. Existing methods usually focus on capturing temporal correlations and ignore the underlying spatial correlations in historical loads, which is crucial for accurate forecasting. In this paper, we propose a novel spatial-temporal gated fusion network (STGNet) for short-term residential load forecasting including not only individual residential load but also aggregated load. By seeking both adaptive neighbors and temporal similarity neighbors, we construct a dynamic graph based on a data-driven method. On this basis, we provide an adaptive graph gated fusion mechanism to capture fine-grained node-specific spatial dependence more accurately and comprehensively. Finally, a bidirectional gated recurrent unit (BiGRU) is utilized to learn the temporal dependence of load data. Extensive experiments on real-world datasets are conducted and demonstrate the superiority of STGNet over the existing approaches. This study proposed a novel spatial-temporal model STGNet for short-term residential load forecasting including both the individual load and the aggregated load. STGNet constructs a dynamic graph based on a data-driven method and provides an adaptive graph gated fusion mechanism to capture fine-grained node-specific spatial dependence more accurately and comprehensively. Bidirectional gated recurrent unit is utilized to learn the temporal dependence of load data. Extensive experiments on real-world datasets demonstrate the superiority of STGNet.image