Short-term wind power forecasting using Gaussian processes

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

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

30 Citations (Scopus)


Since wind has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safety and economics of wind energy utilization. In this paper, we investigate a combination of numeric and probabilistic models: one-day-ahead wind power forecasts were made with Gaussian Processes (GPs) applied to the outputs of a Numerical Weather Prediction (NWP) model. Firstly 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 the turbine controlling strategy, a Censored GP was used to model the relationship between the corrected wind speed and power output. To validate the proposed approach, two real world datasets were used for model construction and testing. The simulation results were compared with the persistence method and Artificial Neural Networks (ANNs); the proposed model achieves about 11% improvement in forecasting accuracy (Mean Absolute Error) compared to the ANN model on one dataset, and nearly 5% improvement on another.
Original languageEnglish
Title of host publicationProceedings of the twenty-third International Joint Conference on Artificial Intelligence
Place of PublicationUnited States
PublisherAAAI Press
Number of pages7
ISBN (Print)978-1-57735-633-2
Publication statusPublished - 2013


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