Wind power day-ahead prediction with cluster analysis of NWP
Lei Dong,
Lijie Wang,
Shahnawaz Farhan Khahro,
Shuang Gao and
Xiaozhong Liao
Renewable and Sustainable Energy Reviews, 2016, vol. 60, issue C, 1206-1212
Abstract:
The selection of training data for establishing a model directly affects the prediction precision. Wind power has the characteristic of daily similarity. The corresponding meteorological data also has the characteristic of daily similarity. This paper proposes a new model with cluster analysis of the numerical weather prediction information. The similar day with the predicted day is searched as training sample to a generalized regression neural network model. The numerical weather prediction data and actual wind power data from a wind farm are used in this study to test the model. The prediction results show that correct cluster analysis method is a useful tool in day-ahead wind power prediction.
Keywords: Wind power prediction; Numerical weather prediction; Cluster analysis; Modeling; Daily similarity (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (42)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:60:y:2016:i:c:p:1206-1212
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DOI: 10.1016/j.rser.2016.01.106
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