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Power Generation Efficiency Prediction Methods of Photovoltaic Panel under Dust Based on Convolutional Neural Network

Dust on the surface of photovoltaic panels can cause the reduction of power generation efficiency and therefore impact efficiency of photovoltaic power plants. A prediction model based on convolutional neural network by taking image of dust status as inputs and power generation efficiency as outputs is established. The data sets of “Dust status image-Power generation efficiency” used for prediction models are collected from actual data acquisition experiments. Prediction models including four classical convolutional neural network models, such as AlexNet, VGG16, ResNet34, ResNet50, and V1, V2, V3 based on VGG16 were trained by acquired data set. The result demonstrates that the proposed V2 model with relatively simple structure and suitable learning step can accurately and quickly predict power generation efficiency of dust status image of photovoltaic panel. In addition, the proposed model provides technical support for photovoltaic power plants to improve power generation efficiency and economic benefits.