County-Level Distributed PV Day-Ahead Power Prediction Based on Grey Correlation Analysis and Transformer-GCAN Model

Le résumé fourni par la source

The distributed photovoltaic (PV) power stations within the entire county exist spatiotemporal correlation. Merely considering temporal correlation makes it challenging to meet the day-ahead scheduling demands. This paper proposes a distributed PV county-level day-ahead power prediction method based on grey relational analysis and the Transformer-Graph Convolutional Attention Network (Transformer-GCAN) model. Firstly, the grey relational degree is used to measure the relevance between each distributed PV stations, and the connection relationship of the station graph is determined based on the analysis results. Secondly, the Transformer network is utilized to extract the temporal features of each PV sequence in the graph. Based on the Graph Convolutional Network (GCN) model, a Graph Attention Mechanism (GAT) is introduced to dynamically extract spatial features between each photovoltaic station in the graph. Finally, the integration of spatiotemporal features is achieved through a fully connected neural network, enabling day-ahead power prediction at the county level. Case analysis results demonstrate that compared with the Transformer-GCN model, the Root Mean Square Error (RMSE) of the power prediction model proposed in this paper is reduced by 11.90%, 15.72% and 19.61% respectively in sunny days, cloudy days and rainy days.

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