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Deep belief network based k-means cluster approach for short-term wind power forecasting

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  • Wang, Kejun
  • Qi, Xiaoxia
  • Liu, Hongda
  • Song, Jiakang

Abstract

Wind energy is the intermittent energy and its output has great volatility. How to accurately predict wind power output is a problem that many researchers have been paying attention to and urgently need to solve. In this paper, a deep belief network (DBN) model is developed for wind power forecasting. The numerical weather prediction (NWP) data was selected as the input of the proposed model and the data directly affects the prediction precision. The NWP data and wind data in the wind farm have the similar characteristics. Therefore, in this paper the k-means clustering algorithm was joined to deal with the NWP data. Through clustering analysis, a large number of NWP samples, which has the great influence in forecasting accuracy, are chosen as the input of the DBN model to improve the efficiency of the model. The DBN model was validated by the Sotavento wind farm in Spain. The results of DBN forecasting were compared with those of Back-propagation neural network (BP) and Morlet wavelet neural network (MWNN). The results show that the forecasting error of DBN model was mostly at a small level, and the forecasting accuracy of the proposed method outperforms BP and MWNN by more than 44%.

Suggested Citation

  • Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pa:p:840-852
    DOI: 10.1016/j.energy.2018.09.118
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    1. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    2. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    3. Dong, Lei & Wang, Lijie & Khahro, Shahnawaz Farhan & Gao, Shuang & Liao, Xiaozhong, 2016. "Wind power day-ahead prediction with cluster analysis of NWP," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1206-1212.
    4. Brayshaw, David James & Troccoli, Alberto & Fordham, Rachael & Methven, John, 2011. "The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK," Renewable Energy, Elsevier, vol. 36(8), pages 2087-2096.
    5. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    6. Ziel, Florian & Croonenbroeck, Carsten & Ambach, Daniel, 2016. "Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity," Applied Energy, Elsevier, vol. 177(C), pages 285-297.
    7. de Jong, Pieter & Kiperstok, Asher & Sánchez, Antonio Santos & Dargaville, Roger & Torres, Ednildo Andrade, 2016. "Integrating large scale wind power into the electricity grid in the Northeast of Brazil," Energy, Elsevier, vol. 100(C), pages 401-415.
    8. Ambach, Daniel & Schmid, Wolfgang, 2017. "A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting," Energy, Elsevier, vol. 135(C), pages 833-850.
    9. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    10. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    11. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    12. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
    13. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    14. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    15. Sharifian, Amir & Ghadi, M. Jabbari & Ghavidel, Sahand & Li, Li & Zhang, Jiangfeng, 2018. "A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data," Renewable Energy, Elsevier, vol. 120(C), pages 220-230.
    16. He, Qingqing & Wang, Jianzhou & Lu, Haiyan, 2018. "A hybrid system for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 226(C), pages 756-771.
    17. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    18. Lazić, Lazar & Pejanović, Goran & Živković, Momčilo, 2010. "Wind forecasts for wind power generation using the Eta model," Renewable Energy, Elsevier, vol. 35(6), pages 1236-1243.
    19. Hu, Qinghua & Zhang, Rujia & Zhou, Yucan, 2016. "Transfer learning for short-term wind speed prediction with deep neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 83-95.
    20. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    21. Xia, Shiwei & Chan, K.W. & Luo, Xiao & Bu, Siqi & Ding, Zhaohao & Zhou, Bin, 2018. "Optimal sizing of energy storage system and its cost-benefit analysis for power grid planning with intermittent wind generation," Renewable Energy, Elsevier, vol. 122(C), pages 472-486.
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