An Ensemble Neural Network Based on Variational Mode Decomposition and an Improved Sparrow Search Algorithm for Wind and Solar Power Forecasting

Accurate forecasting methods for wind and solar power are important for power systems because of their potential to improve the economic and environmental performance. For this purpose, an ensemble neural network framework composed of LSTM, SVM, BP neural network, and ELM is proposed for wind and solar power forecasting in China. Three common methods for improving the prediction accuracy were adopted. First, unstable wind and solar power time series are decomposed into smooth subsequences by VMD, which reduces the undesirable effects caused by the volatility of the original series. Then, based on the decomposed subsequences, four basic models that are optimized based on the EOSSA algorithm are used to forecast wind and solar power. Finally, the prediction results of ENN were reconstructed by weighting the prediction results of the four models. The proposed ENN model was compared to nine state-of-the-art prediction models for wind and solar power forecasting. The results showed that the ENN model had the lowest MAPE, MAE, MSE, and RMSE for both wind and solar power forecasting. These comparison results also show that the ENN model not only has the best prediction accuracy, but also the most reliable prediction performance.