Deep Learning Ensemble Based New Approach for Very Short-Term Wind Power Forecasting
Deep Learning Ensemble Based New Approach for Very Short-Term Wind Power Forecasting
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DOI：
10.1109/pesgm41954.2020.9281473
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Dan A. Rosa de Jesús;P. Mandal;Yuan-Kang Wu;T. Senjyu
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文献类型：
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作者：
Dan A. Rosa de Jesús;P. Mandal;Yuan-Kang Wu;T. Senjyu
本文提出了一种基于深度学习集合的新预测方法，用于在1小时，3H和6H的外观期间预测的非常短期（10分钟）风能预测。集合，特别是HDNN+CNN，HDNN+LSTM，CNN+LSTM和HDNN+CNN+LSTM，该方法是通过启用效率的最终效果的效力，将风速的历史数据视为风速的主要输入。反过来帮助提高从德克萨斯州的真实风电场获得的最终预测准确性。
This paper presents a new prediction approach based on deep learning ensemble for very short-term (10-minuteahead) wind power forecasting for a look-ahead period of 1h, 3h, and 6h. The proposed deep learning ensemble approach combines several individual and hybrid deep learning models, such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Hybrid Deep Neural Network (HDNN), with the formation of four different ensembles, in particular HDNN+CNN,HDNN+LSTM, CNN+LSTM, and HDNN+CNN+LSTM. The proposed approach considers the historical data of wind speed as major input through ensemble averaging in order to produce the final wind power prediction. The major advantage of the proposed ensemble learning is that they make the best use of predictions from multiple deep learning models and their capability to effectively “cancel out” the individual errors, which in turn help enhance the final prediction accuracy. The simulation on actual data, acquired from the real wind farm in Texas, demonstrates the effectiveness of the presented approach to produce a higher degree of very short-term wind power forecast accuracy for multiple seasons of the year in comparison to other soft computing as well as to individual deep learning models.