期刊
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
卷 113, 期 -, 页码 515-527出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2019.05.075
关键词
Fuzzy information granularity (FIG); Support vector quantile regression (SVQR); Wind and solar power; Probability density function
资金
- National Natural Science Foundation [71771073, 71401049, U1765201, 71671056]
- CRSRI Open Research Program [SN CKWV2017525/KY]
- Open Research Fund of State Key Laboratory of simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research) [IWHR-SKL-201605]
Uncertainty among wind and solar power affects the stability of power systems. In order to fully describe the uncertainty of wind and solar power, a probability density prediction model is proposed to predict the probability density function of wind and solar power. According to the wind and solar power time series, the original data is processed with fuzzy information granularity to eliminate the fluctuation and uncertainty of data. The Lagrange function is constructed by a support vector quantile regression model to get the quantile of wind and solar power at different points. The conditional quantiles are combined with the Epanechnikov kernel function to acquire complete probability density curves of forecasting results. In order to evaluate the performance of the output results, this paper analyzes the accuracy of the prediction results using point prediction error, prediction interval coverage probability and average bandwidth. The experimental data of wind and solar power with same temporal and spatial resolutions are taken into account. The results show that the method can effectively describe the uncertainty of wind and solar power, and also provide technical support for the safe and stable operation of the power system.
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