文章摘要
赵书强,张婷婷,李志伟,等.基于数值特性聚类的日前光伏出力预测误差分布模型[J].电力系统自动化,2019,43(13):36-45. DOI: 10.7500/AEPS20180405002.
ZHAO Shuqiang,ZHANG Tingting,LI Zhiwei, et al.Distribution Model of Day-ahead Photovoltaic Power Forecasting Error Based on Numerical Characteristic Clustering[J].Automation of Electric Power Systems,2019,43(13):36-45. DOI: 10.7500/AEPS20180405002.
基于数值特性聚类的日前光伏出力预测误差分布模型
Distribution Model of Day-ahead Photovoltaic Power Forecasting Error Based on Numerical Characteristic Clustering
DOI:10.7500/AEPS20180405002
关键词: 光伏发电出力  日前预测误差  通用型高斯混合分布  模糊C均值聚类
KeyWords: photovoltaic power output  day-ahead forecasting error  general Gauss mixed model  fuzzy C-means clustering
上网日期:2019-02-26
基金项目:国家重点研发计划资助项目(2017YFB0902200);国家电网公司科技项目(5228001700CW);中央高校基本科研业务费专项资金资助项目(2018QN074)
作者单位E-mail
赵书强 新能源电力系统国家重点实验室(华北电力大学), 河北省保定市 071003  
张婷婷 新能源电力系统国家重点实验室(华北电力大学), 河北省保定市 071003 769890610@qq.com 
李志伟 新能源电力系统国家重点实验室(华北电力大学), 河北省保定市 071003  
李东旭 新能源电力系统国家重点实验室(华北电力大学), 河北省保定市 071003  
许晓艳 中国电力科学研究院有限公司, 北京市 100192  
刘金山 国网青海省电力公司, 青海省西宁市 810000  
摘要:
      光伏出力预测误差难以避免且不容忽视,预测误差分布的准确描述有利于电力系统的优化调度和稳定运行。基于此,分析预测误差分布与其影响因素之间的相关性,提出一种基于数值特性聚类的日前光伏出力预测误差概率模型。利用模糊C均值聚类法对预测误差的整体水平进行分类,再依据预测出力的数值特性进行分区处理,并建立了适用于估计误差分布的通用型高斯混合模型。该分析方法综合考虑了气象因素和预测出力数值特性对预测误差的影响,可以较为准确地估计不同时刻的预测误差,给出预测误差分布的置信区间,且不受预测算法和光伏电站地理信息的限制。基于比利时和中国西北地区光伏系统历史数据的分析结果表明,所提误差模型可描述光伏出力预测误差分布偏态性和峰度多样性,效果优于其他分布模型,能够用于描述不同情况下的日前光伏出力预测误差分布。
Abstract:
      The forecasting error of photovoltaic(PV)output is difficult to avoid and cannot be ignored. The accurate description of the forecasting error distribution is beneficial to the optimal dispatch and stable operation of the power system. The correlation between the forecasting error distribution and influencing factors is analyzed, and a probabilistic model of the PV output forecasting error based on numerical characteristic clustering is proposed. The whole level of forecasting errors will be classified through the fuzzy C-means clustering and the classification on the output forecasting will be conducted according to its numerical characteristics. A general Gauss mixed model available to estimate error distribution is established. Considering the influences of meteorological factors and numerical characteristics on forecasting errors, the model could estimate forecasting errors at different times and produce the confidence interval of error distribution. At the same time, the proposed model is free from the influences brought by different forecasting algorithms and the geographic information of PV power station. Based on the historical data of PV systems in Belgium and Northwest China, analysis results show that the skewness, multi-peak and kurtosis diversity of PV power forecasting error distribution can be described by the proposed model more accurately compared with other distribution models, which can be used to describe the forecasting error distribution of day-ahead PV power in different situations.
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