Feature Extraction of NWP Data for Wind Power Forecasting Using 3D-Convolutional Neural Networks

Wind power is one of the most attractive forms of electricity from the viewpoints of cost efficiency and environmental protection. However, the instability of wind power has a serious impact on a grid system. Reliable wind power forecasting will help to utilize storage systems and backup generators effectively for mitigating the instability. This paper proposes a feature extraction procedure for numerical weather prediction (NWP) data based on the three-dimensional convolutional neural networks (3D-CNNs). An advantage of 3D-CNNs is to automatically extract the spatio-temporal features from NWP data focusing on the targeted wind farm. Feature extraction based on 3D-CNNs was applied to real-world datasets; the results show significant performance in comparison to several benchmark approaches, and also show that the proposed extraction scheme based on 3D-CNNs achieves to derive intrinsic features for prediction of wind power generation from NWP data. (C) 2018 The Authors. Published by Elsevier Ltd.