Special condition wind power forecasting based on Gaussian process and similar historical data

Juan Yan, Kang Li, Er-Wei Bai, Aoife Foley

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)


Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.
Original languageEnglish
Publication statusPublished - Jul 2015
EventIEEE Power & Energy Society General Meeting - Colorado, Denver, United States
Duration: 27 Jul 201531 Jul 2015


ConferenceIEEE Power & Energy Society General Meeting
Country/TerritoryUnited States
Internet address


  • Wind power, Gaussian Process, Similar Pattern, Forecasting


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