Short-Term Wind Power Forecasting Using Mixed Input Feature-Based Cascade-connected Artificial Neural Networks - Chen, Qin; Folly, Komla Agbenyo (Frontiers in Energy Research, 2021)
Accurate short-term wind power forecasting is crucial for the efficient operation of the power system with high wind power penetration. Many forecasting approaches have been developed in the past to forecast short-term wind power. In recent years, artificial neural networks-based approaches (ANNs) have become one of the most effective and popular approaches for short-term wind speed and wind power forecasting. However, most researchers have used only historical data from a specific station to train the ANNs without considering the effect of meteorological variables from many neighboring stations on the forecasting performance. Using additional meteorological variables from neighboring stations can contribute valuable surrounding information to the forecasting model of the target station and improve the algorithm's performance. In this paper, a mixed input features-based cascade-connected artificial neural network (MIF-CANN) is used to train input features from neighboring stations without encountering overfitting issues. Different combinations of input features are trained by multiple ANNs in the first layer of the MIF-CANN model to produce preliminary results, which are then cascaded into the second phase of the MIF-CANN model as inputs. The performance of the proposed MIF-CANN model is compared with the ANNs-based spatial correlation models. Simulation results show that the proposed MIF-CANN has better performance than the ANNs-based spatial correlation models.