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                Suzhou Electric Appliance Research Institute
                期刊号: CN32-1800/TM| ISSN1007-3175

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                风电功率及其〓预测误差概率分布研究

                来源:yabo22vip亚博发布时间:2021-09-18 13:18 浏览次数:23

                风电功率及其预测误差概率分布研究

                谢彦祥
                (中国电力工程顾问集团西南电力设计院有限公↑司,四川 成都 610021)
                 
                    摘 要:进行风电功率及其预测々误差概率分布研究对分析风电功率分∞布特性有重要意义。以风☆电功率、日功率波动量均值为指标,统计分析风电在不同时间尺度下的波动概率√分布;针对正态分布模 型对风电功率及其预测误差分布拟合效∞果较差问题,利用非参数估计法拟合风电功率及其短期预测误差概率分布,并以残差平方和、相关系数为评价指标,对比不同预测模型和采样间隔对应的拟合效果;基于实测数据的分析结果表明,非参数估计法可以有效拟合风电功率及其短期预测误差概率分布,且具有较好的实用性。
                    关键词:风电功率;概率分布;短期预测;预测误差分布;非参数估计
                    中图分类号:TM614     文献标识码:A     文章编号:1007-3175(2021)09-0007-07
                 
                Study on Probability Distribution of Wind Power and Its Forecasting Error
                 
                XIE Yan-xiang
                (Southwest Electric Power Design Institute Co., Ltd. of CPECC, Chengdu 610021, China)
                 
                    Abstract: The investigation of the distribution of wind power and its prediction error probability is necessary. It has a significant meaning to the analysis of wind power distribution characteristics. In this paper, the average value of wind power and daily power fluctuations were used as indicators to statistically analyze the probability distribution of wind power fluctuations on different time scales. The normal distribution model has problems of poor-fitting effect on wind power and prediction error distribution. This study used a non-parametric estimation method to fit wind power and its short-term prediction error probability distribution. It also used the residual sum of squares and correlation coefficient as evaluation indicators to compare the fitting effects of different prediction models and sampling intervals. The analysis result based on the measured data shows that the non-parametric estimation method can effectively fit the probability distribution of wind power and its short-term forecast error, and it has well practicability.
                    Key words: wind power; probability distribution; short-term forecast; forecast error distribution; non-parametric estimation
                 
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