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

Kejun Wang, Xiaoxia Qi, Hongda Liu and Jiakang Song

Energy, 2018, vol. 165, issue PA, 840-852

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%.

Keywords: Wind power forecasting; Numerical weather prediction; k-means clustering; Deep learning; Deep belief network (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (68)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218318826
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:165:y:2018:i:pa:p:840-852

DOI: 10.1016/j.energy.2018.09.118

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().