Short-Term Wind Power Prediction Based on Similar Day and Feature Extraction
[1]ZHANG Yingchao,CHENG Jinjie,DENG Hua,et al.Short-Term Wind Power Prediction Based on Similar Day and Feature Extraction[J].Journal of Zhengzhou University (Engineering Science),2020,41(05):44-49.[doi:10.13705/j.issn.1671-6833.2020.02.023]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41卷
Number of periods:
2020 05
Page number:
44-49
Column:
Public date:
2020-10-01
- Title:
- Short-Term Wind Power Prediction Based on Similar Day and Feature Extraction
- Keywords:
- discrete Fréchet distance; similar day; kernel entropy component analysis; support vector machine; power prediction
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2020.02.023
- Abstract:
- In order to improve the short-term wind power prediction accuracy and enhance the representativeness and adaptability of the prediction model to specific weather conditions, a data processing method was proposed based on discrete Fréchet distance and kernel entropy component analysis (KECA).Using Fréchet distance, this paper established a mathematical model matching similar days, extracted samples similar to the prediction date, and then used KECA to extract suitable nonlinear principal elements from various meteorological elements as input of support vector machine (SVM) model. The simulation verification showed that the proposed method could significantly improve the prediction’s accuracy and has certain applicability.
- References:
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Last Update:
2020-10-23