A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting

Hui Liu, Chengqing Yu, Haiping Wu, Zhu Duan and Guangxi Yan

Energy, 2020, vol. 202, issue C

Abstract: Wind speed forecasting is a promising solution to improve the efficiency of energy utilization. In this study, a novel hybrid wind speed forecasting model is proposed. The whole modeling process of the proposed model consists of three steps. In stage I, the empirical wavelet transform method reduces the non-stationarity of the original wind speed data by decomposing the original data into several sub-series. In stage II, three kinds of deep networks are utilized to build the forecasting model and calculate prediction results of all sub-series, respectively. In stage III, the reinforcement learning method is used to combine three kinds of deep networks. The forecasting results of each sub-series are combined to obtain the final forecasting results. By comparing all the results of the predictions over three different types of wind speed series, it can be concluded that: (a) the proposed reinforcement learning based ensemble method is effective in integrating three kinds of deep network and works better than traditional optimization based ensemble method; (b) the proposed ensemble deep reinforcement learning based wind speed prediction model can get accurate results in all cases and provide the best accuracy compared with sixteen alternative models and three state-of-the-art models.

Keywords: Wind speed forecasting; Ensemble deep reinforcement learning; Empirical wavelet transform; Hybrid wind speed forecasting model (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (39)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:202:y:2020:i:c:s0360544220309014

DOI: 10.1016/j.energy.2020.117794

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