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Proceedings Paper

Super short term forecasting of photovoltaic power generation output in micro grid
Author(s): Cheng Gong; Longfei Ma; Zhongjun Chi; Baoqun Zhang; Ran Jiao; Bing Yang; Jianshu Chen; Shuang Zeng
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Paper Abstract

The prediction model combining data mining and support vector machine (SVM) was built. Which provide information of photovoltaic (PV) power generation output for economic operation and optimal control of micro gird, and which reduce influence of power system from PV fluctuation. Because of the characteristic which output of PV rely on radiation intensity, ambient temperature, cloudiness, etc., so data mining was brought in. This technology can deal with large amounts of historical data and eliminate superfluous data, by using fuzzy classifier of daily type and grey related degree. The model of SVM was built, which can dock with information from data mining. Based on measured data from a small PV station, the prediction model was tested. The numerical example shows that the prediction model is fast and accurate.

Paper Details

Date Published: 23 January 2017
PDF: 7 pages
Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103223V (23 January 2017); doi: 10.1117/12.2266068
Show Author Affiliations
Cheng Gong, State Grid Beijing Electric Power Research Institute (China)
Longfei Ma, State Grid Beijing Electric Power Reseaech Institute (China)
Zhongjun Chi, State Grid Beijing Electric Power Reseaech Institute (China)
Baoqun Zhang, State Grid Beijing Electric Power Reseaech Institute (China)
Ran Jiao, State Grid Beijing Electric Power Reseaech Institute (China)
Bing Yang, State Grid Beijing Electric Power Reseaech Institute (China)
Jianshu Chen, State Grid Beijing Electric Power Reseaech Institute (China)
Shuang Zeng, State Grid Beijing Electric Power Reseaech Institute (China)


Published in SPIE Proceedings Vol. 10322:
Seventh International Conference on Electronics and Information Engineering
Xiyuan Chen, Editor(s)

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