Conference proceeding
Special condition wind power forecasting based on Gaussian Process and similar historical data
2015 IEEE Power & Energy Society General Meeting, Vol.2015-, pp.1-5
07/2015
DOI: 10.1109/PESGM.2015.7285985
Abstract
Due to the variability of wind power, it is imperative to accurately and timely forecast wind generation to enhance the flexibility and reliability of the operation and control of real-time power systems. Special events such as ramps and 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 historical dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the historical data with similar patterns are selected and then embedded in Gaussian Process model to make new 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 the magnitude error but also the phase error.
Details
- Title: Subtitle
- Special condition wind power forecasting based on Gaussian Process and similar historical data
- Creators
- Juan Yan - Queen's University BelfastKang Li - Queen's University BelfastErwei Bai - University of IowaAoife Foley - Queen's University Belfast
- Resource Type
- Conference proceeding
- Publication Details
- 2015 IEEE Power & Energy Society General Meeting, Vol.2015-, pp.1-5
- DOI
- 10.1109/PESGM.2015.7285985
- ISSN
- 1932-5517
- eISSN
- 1944-9933
- Publisher
- IEEE
- Language
- English
- Date published
- 07/2015
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197543202771
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