A Method for Short-Term Wind Power Prediction With Multiple Observation Points

被引:140
作者
Khalid, Muhammad [1 ]
Savkin, Andrey V. [1 ]
机构
[1] Univ New S Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia
基金
澳大利亚研究理事会;
关键词
Adaptive filtering; networked systems; prediction; renewable energy; wind power; NEURAL-NETWORKS; ENERGY; GENERATION; SYSTEMS; MODELS; SPEED;
D O I
10.1109/TPWRS.2011.2160295
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
This paper presents a method to improve the short-term wind power prediction at a given turbine using information from numerical weather prediction and from multiple observation points, which correspond to the locations of nearby turbines at a particular wind farm site. The prediction of wind power is achieved in two stages; in the first stage wind speed is predicted using our proposed method. In the second stage, the wind speed to output power conversion is accomplished using power curve model. The proposed wind power prediction method is tested using real measurements and NWP data from one of the wind farm sites in Australia. The performance is compared with the persistence and Grey predictor model in terms of Mean Absolute Error and Root Mean Square Error.
引用
收藏
页码:579 / 586
页数:8
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