Hour-ahead photovoltaic power forecast using a hybrid GRA-LSTM model based on multivariate meteorological factors and historical power datasets

Abstract Owing to the clean, inexhaustible and pollution-free, solar energy has become a powerful means to solve energy and environmental problems. However, photovoltaic (PV) power generation varies randomly and intermittently with respect to the weather, which bring the challenge to the dispatching of PV electrical power. Thus, power forecasting for PV power generation has become one of the key basic technologies to overcome this challenge. The paper presents a grey relational analysis (GRA) and long short-term memory recurrent neural network (LSTM RNN) (GRA-LSTM) model-based power short-term forecasting of PV power plants approach. The GRA algorithm is adopted to select the similar hours from history dataset, and then the LSTM NN maps the nonlinear relationship between the multivariate meteorological factors and power data. The proposed model is verified by using the dataset of the PV systems from the Desert Knowledge Australia Solar Center (DKASC). The prediction results of the method are contrasted with those obtained by LSTM, grey relational analysis-back propagation neural network (GRA-BPNN), grey relational analysis-radial basis function neural network (GRA-RBFNN) and grey relational analysis-Elman neural network (GRA-Elman), respectively. Results show an acceptable and robust performance of the proposed model.