Distributed Photovoltaic Short-Term Power Prediction Based on Personalized Federated Multi-Task Learning

In a distributed photovoltaic system, photovoltaic data are affected by heterogeneity, which leads to the problems of low adaptability and poor accuracy of photovoltaic power prediction models. This paper proposes a distributed photovoltaic power prediction scheme based on Personalized Federated Multi-Task Learning (PFL). The federal learning framework is used to enhance the privacy of photovoltaic data and improve the model's performance in a distributed environment. A multi-task module is added to PFL to solve the problem that an FL single global model cannot improve the prediction accuracy of all photovoltaic power stations. A cbam-itcn prediction algorithm was designed. By improving the parallel pooling structure of a time series convolution network (TCN), an improved time series convolution network (iTCN) prediction model was established, and the channel attention mechanism CBAMANet was added to highlight the key meteorological characteristics' information and improve the feature extraction ability of time series data in photovoltaic power prediction. The experimental analysis shows that CBAM-iTCN is 45.06% and 42.16% lower than a traditional LSTM, Mae, and RMSE. Compared with FL, the MAPE of the PFL proposed in this paper is reduced by 9.79%, and for photovoltaic power plants with large data feature deviation, the MAPE experiences an 18.07% reduction.