Prediction of power generation and maintenance using AOC‐ResNet50 network - Yueqiang Chu; Wanpeng Cao; Cheng Xiao; Yubin Song (IET Renewable Power Generation)
Abstract With the continuous expansion of the photovoltaic industry, the application of solar photovoltaic power generation systems is becoming increasingly widespread. Due to the obvious intermittency and volatility of photovoltaic power generation, integration of large‐scale photovoltaic power generation into the power grid can cause certain impacts on the security and stability of the grid. Photovoltaic power prediction is essential to solve this problem, as it can improve the quality of photovoltaic grid connection, optimize grid scheduling, and ensure the safe operation of the grid. In this article, the deep learning method is selected for photovoltaic power prediction. Based on the analysis of the OctConv (Octave Convolution) network structure, the AOctConv (Attention Octave Convolution) convolutional neural network structure is proposed, which is combined with the ResNet50 backbone network to obtain AOC‐ResNet50. It is then applied to the prediction of the generation of photovoltaic power. The prediction performance is compared with the ResNet50 network and the Oct‐ResNet50 network, and it is found that the AOC‐ResNet50 network has the best prediction performance, with an MAE of only 0.176888. Based on the exemplar work, a framework is proposed to illustrate this method. Its general application is discussed.