Deep learning based ensemble approach for probabilistic wind power forecasting

By Huaizhi Wang, Gangqiang Li, Guibin Wang, Jianchun Peng, Hui Jiang, Yitao Liu. 2017

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On-Line Building Energy Optimization Using Deep Reinforcement Learning (2017, background): “Possible benefits of deep learning methods in terms of superior accuracy start to be investigated also for other types of signals in smart grids, such as wind forecast [32], as well as for event detection (classification) problem [33].”
A Review of Deep Learning Models for Time Series Prediction (2019, background): “An ensemble model is established in [79] for wind power forecasting.”
A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting (2019, methodology): “Because CNN adopts the weight sharing technique, so it has fewer parameters than the previous method, and it also can effectively extract the intrinsic features in wind power data [26].”