Behind-the-Meter Solar Generation Disaggregation Based on Attention Mechanism and Bidirectional Gated Recurrent Unit

With the rising installed capacity of rooftop PV, there is an urgent need to improve the accuracy of the behind-the-meter solar generation decomposition to realize the local consumption of distributed PV in order to alleviate the grid stability problem caused by the large-scale rooftop PV access. This paper proposes a bidirectional Gated Recurrent Unit neural network based on the Attention mechanism for a behind-the-meter solar generation decomposition model. First, the temporal characteristics of customer net load data are extracted using bidirectional gated recurrent units. Then, the Attention mechanism is introduced to improve the attention to key net load information. Finally, a nonlinear mapping relationship is constructed from net load data to behind-the-meter solar generation. 184 household PV customers in the SGSC dataset are used for the example analysis. The simulation results show that the proposed method does not rely on accurate physical modeling as well as accurate numerical weather forecast data, and has good generalization in scenarios of different climate zones and good adaptability in scenarios of different seasons.