A novel hybrid forecasting system based on data augmentation and deep learning neural network for short-term wind speed forecasting

Abstract

As a clean, economical, and renewable energy source, wind energy plays a very important role in easing the shortage of fossil energy, environmental population, and climate change. However, due to the strong intermittency, volatility, and randomness of wind speed, the large-scale connection of wind energy into the power grid is restricted. Therefore, constructing a reliable prediction model to achieve high-accuracy wind speed prediction is necessary. For this purpose, a novel hybrid model for short-term wind speed prediction is proposed in this paper. First, empirical mode decomposition is employed to decompose the raw wind speed time series into a set of subseries. Then, a data augmentation technique is first used to generate more training data to avoid overfitting of the prediction model. Furthermore, a new predictor based on a convolutional neural network (CNN), a long short-term memory (LSTM) network, and an extreme learning machine (ELM) is proposed for deterministic wind speed prediction, where a fuzzy entropy-based partition strategy is implemented to assign subseries to the CNN-LSTM and ELM. To improve the prediction performance, a synchronous optimization method based on an improved hybrid particle swarm optimization/gray wolf optimizer is proposed for feature selection and parameter optimization. Afterward, kernel density estimation is used to estimate the wind speed probability density function for probabilistic prediction. Finally, the performance of the proposed model is compared with seven other models by using three wind speed datasets from four aspects: point prediction, interval prediction, probability prediction comprehensive performance, and prediction reliability. The experimental results show that the proposed method achieves excellent performance on wind speed time series prediction.