Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Lilin Cheng, Haixiang Zang, Tao Ding, Rong Sun, Miaomiao Wang, Zhinong Wei and Guoqiang Sun
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Lilin Cheng: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Haixiang Zang: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Tao Ding: Department of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Rong Sun: Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China
Miaomiao Wang: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Zhinong Wei: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Guoqiang Sun: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China

Energies, 2018, vol. 11, issue 8, 1-23

Abstract: Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as sub-models for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deep-learning-based sub-models. Lastly, variances are obtained from sub-models and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

Keywords: recurrent neural network; adaptive neuro fuzzy inference system; probabilistic wind speed forecasting; deep learning; ensemble learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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