Photovoltaic Forecasting with a Connected Multi-Structure Neural Network

Photovoltaic power generation is one of the renewable energy sources, but it is influenced by factors such as weather, season, and day night changes. Accurate predictions can help photovoltaic power plant managers better plan and adjust power generation to maximize the energy utilization efficiency of solar panels. If the output of photovoltaic power generation can be accurately predicted, it can better match the demand of the power grid and reduce energy waste. This helps to reduce the problem of load imbalance in the power system and improve the stability of the power grid. In addition, the operation and maintenance costs of photovoltaic power plants are high. By making accurate predictions, the operations team can more effectively arrange maintenance work, reduce unnecessary downtime, and thus reducing operational costs. However, the current traditional prediction methods cannot adapt well to this rapidly expanding market demand, and the accuracy of their predictions is not very good, which to some extent affects the development of related industries. To this end, we have built a CNN double _ GRU model, whose first layer is a convolutional layer, is used to extract universal meteorological data features from input data. Next are two GRU layers that return the output of each time step and the output of the last time step, which helps to capture long-term dependencies in the sequence. A Dropout layer was set between two GRU layers to reduce overfitting. Next, this paper sets the Flatten layer to flatten the output of the GRU layer, and finally performs regression prediction in the fully connected layer. The goal of the entire model is to map the input time series data to a single output that represents the prediction of photovoltaic power generation. During the training process, the model uses a mean square error loss function to measure the difference between the predicted value and the actual value, and updates the model parameters through the Adam optimizer. Through experiments, it has been shown that this model has improved in multiple indicators compared to common traditional models and neural network models in predicting photovoltaic power generation.