Wind power forecasts using Gaussian processes and numerical weather prediction

Niya Chen, Zheng Qian, Ian T. Nabney, Xiaofeng Meng

Research output: Contribution to journalArticlepeer-review


Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data.

Original languageEnglish
Pages (from-to)656-665
Number of pages10
JournalIEEE Transactions on Power Systems
Issue number2
Early online date2 Oct 2013
Publication statusPublished - 1 Mar 2014

Bibliographical note

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Funding: National Fund for Creative Groups of China (Grant No. 61121003) and in part by a BUAA Scholarship


  • censored data
  • Gaussian process
  • numerical weather prediction
  • wind power forecasting


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