Short-term power prediction for photovoltaic power plants using a hybrid improved Kmeans-GRA-Elman model based on multivariate meteorological factors and historical power datasets.

Highlights • Short-term PV power prediction model using favorable historical data is presented. • GRA and K-means++ algorithms are used to find similar days and optimal similarity. • The samples of similar days and optimal similarity are used to train the Elman NN. • Comparative results show the outstanding performance of the proposed method. Abstract With the continuous consumption of fossil fuels such as coal, oil and natural gas, the environmental energy problem has become the focus of attention in the world. The utilization of clean and non-polluting solar energy for photovoltaic (PV) power generation can effectively utilize renewable energy. However, the instability of weather condition makes the output of PV power have strong randomness, fluctuations and intermittence. Therefore, reliable PV power prediction method can reduce the disadvantages of PV power generation, which is of great significance to maintenance and repair of power plants. In the study, a novel hybrid prediction model combining improved K-means clustering, grey relational analysis (GRA) and Elman neural network (Hybrid improved Kmeans-GRA-Elman, HKGE) for short-term PV power prediction is proposed. The proposed model is established by using multivariate meteorological factors and historical power datasets for two years. The improved K-means approach is applied to cluster the historical power datasets, and combining the GRA method to determine the similarity days and the optimal similarity day of the forecasting day. The Elman neural network model is employed to better develop the nonlinear relationship between multivariate meteorological factors and power data. Compared with the other eight prediction methods, the results show that the proposed method has an outstanding performance on improving the prediction accuracy. [ABSTRACT FROM AUTHOR]