A Hybrid Neural Network-Based Intelligent Forecasting Approach for Capacity of Photovoltaic Electricity Generation

Abstract

In recent years, photovoltaic power generation technology has become the key planning direction of the country. It is important to effectively predict photovoltaic (PV) electricity generation capacity, so that the administrators can well schedule resource allocation. Currently, most of the photovoltaic electricity generation forecasting models took meteorological data as the input parameters of neural network. However, the input parameters and redundant data cause neural network to converge difficultly. Besides, single types of neural network models cannot well capture the comprehensive characteristics, which may influence forecasting effect in evolving process. As a result, we propose a hybrid neural network-based intelligent forecasting approach for PV electricity generation capacity. First, convolution neural network (CNN) is adopted to extract the connection between features and data from the perspective of convolution operations. And then, the extracted feature vector of time series is sent into the long short-term memory (LSTM) model. Finally, the forecasting values are predicated by training the outlined LSTM network. The experimental results indicate that such a hybrid CNN-LSTM model can significantly improve the precision of PV electricity generation prediction and provide an effective way to forecast generation power of PV system.