A load forecasting method for photovoltaic systems based on distributed learning

In the context of global energy transition, China's power industry is facing the challenge of green and low carbon transformation, and it is necessary to optimize the power supply strategy to cope with the problem of power surplus and consumption. The dynamic change of power load demand makes the traditional on-demand generation mode difficult to continue. Among data-driven load forecasting technologies, Gated Recurrent Unit (GRU) has potential application in the field of load forecasting due to its advantages, but data dispersion and privacy issues restrict the performance improvement of the model. As an emerging machine learning paradigm, distributed learning can realize multi-party collaborative training model, protect data privacy and integrate the advantages of multi-source data. Based on the distributed learning framework and the advantages of time series modeling of GRU network, this paper proposes a load forecasting method for photovoltaic system. A distributed learning algorithm is designed to realize efficient collaboration of multiple participants, and the GRU model parameters are optimized by linear aggregation, which provides strong support for optimal scheduling and stable operation of power system.