查看更多>>摘要：The accurate prediction of photovoltaic(PV)power generation is an important basis for hybrid grid scheduling.With the expansion of the scale of PV power plants and the popularization of distributed PV,this study proposes a multilayer PV power generation prediction model based on transfer learning to solve the problems of the lack of data on new PV bases and the low accuracy of PV power generation prediction.The proposed model,called DRAM,concatenates a dilated convolutional neural network(DCNN)module with a bidirectional long short-term memory(BiLSTM)module,and integrates an attention mechanism.First,the processed data are input into the DCNN layer,and the dilation convolution mechanism captures the spatial features of the wide sensory field of the input data.Subsequently,the temporal characteristics between the features are extracted in the BiLSTM layer.Finally,an attention mechanism is used to strengthen the key features by assigning weights to efficiently construct the relationship between the features and output variables.In addition,the power prediction accuracy of the new PV sites was improved by transferring the pre-trained model parameters to the new PV site prediction model.In this study,the pre-training of models using data from different source domains and the correlations between these pre-trained models and the target domain were analyzed.
查看更多>>摘要：To realize carbon neutrality,there is an urgent need to develop sustainable,green energy systems(especially solar energy systems)owing to the environmental friendliness of solar energy,given the substantial greenhouse gas emissions from fossil fuel-based power sources.When it comes to the evolution of intelligent green energy systems,Internet of Things(IoT)-based green-smart photovoltaic(PV)systems have been brought into the spotlight owing to their cutting-edge sensing and data-processing technologies.This review is focused on three critical segments of IoT-based green-smart PV systems.First,the climatic parameters and sensing technologies for IoT-based PV systems under extreme weather conditions are presented.Second,the methods for processing data from smart sensors are discussed,in order to realize health monitoring of PV systems under extreme environmental conditions.Third,the smart materials applied to sensors and the insulation materials used in PV backsheets are susceptible to aging,and these materials and their aging phenomena are highlighted in this review.This review also offers new perspectives for optimizing the current international standards for green energy systems using big data from IoT-based smart sensors.
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