An Ultra-Short-Term and Short-Term Wind Power Forecasting Approach Based on Optimized Artificial Neural Network with Time Series Reconstruction

Wind power forecasting (WPF) is crucial for grid dispatch and effective collection of wind power. In recent years, deep learning such as multi-layer perception (MLP) has been widely adopted in WPF. This paper proposes a novel ultra-short-term and short-term WPF approach based on optimized artificial neural network (ANN) with time series reconstruction (TSR) method. Z-scored method is adopted to preprocess the dataset measured from a wind farm. TSR is proposed and used to generate model inputs. An optimized ANN-based model with MLP architecture is introduced, and momentum algorithm and decaying learning rate are proposed for model optimization. Repeated trainings are performed to obtain the optimal model. The proposed approach is validated on the test case of a wind farm in China. The results obtained prove that the optimized ANN-based model and TSR can both effectively improve the accuracy of ultra-short-term and short-term WPF.