In order to extract effective temporal features and connections between non Euclidean domains from a large amount of historical photovoltaic power generation data,a short-term photovoltaic power prediction model based on mixed graph neural network and gated recurrent network is established.The model first generates the K-nearest neighbor graph of meteorological and output data through the K-nearest neighbor classification algorithm,and then uses the graph neural network as an encoder to encode the meteorological and output data to form a time series,and finally outputs the photovoltaic power prediction results through the gated recurrent network and the full connection layer decoding.Through simulation and analysis,the model has better feature mining ability and analysis performance,can better highlight the meteorological and output data characteristics of a certain time node,adapt to the feature changes caused by sudden changes in weather,and thus improve the expression ability of the overall model of photovoltaic prediction.
关键词
图神经网络/深度学习/光伏发电/功率预测/门控循环网络
Key words
graph neural networks/deep learning/photovoltaic power generation/power forecasting/gated recurrent network
维普
万方数据