| 极值负荷及其出现时刻的概率化预测 |
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| 引用本文： | 陈新宇,康重庆,陈敏杰.极值负荷及其出现时刻的概率化预测[J].中国电机工程学报,2011,31(22). |
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| 作者姓名： | 陈新宇 康重庆 陈敏杰 |
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| 作者单位： | 1. 电力系统及发电设备控制和仿真国家重点实验室（清华大学电机系）,北京市海淀区,100084
2. 麻省理工学院电气工程与计算机科学系,美国马萨诸塞州剑桥市02139 |
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| 基金项目： | 国家自然科学基金项目(51077077,50777031)~~ |
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| 摘 要： | 极值负荷的幅值与出现时刻是决定调度运行计划的重要依据，而其预测精度往往不尽如人意。实现概率化的预测，是规避极值负荷预测不准确所带来的风险的有效途径。以最高负荷为例，深入剖析了极值负荷的多峰特性，分析了幅值与出现时刻的统计规律，建立了日落时刻与晚高峰出现时间之间的回归模型；基于日间极值负荷增量的分类统计，预测下一天的各个子高峰幅值的概率分布（probabilistic density function，PDF），运用序列运算理论计算日最高负荷幅值的概率分布，最终根据全概率公式，实现了日最高负荷出现时刻的概率性预测方法。中国北方某城市的实际预测表明，所提出的概率化预测方法可以有效地解决极值负荷预测问题。
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| 关 键 词： | 电力系统 短期负荷预测 母线负荷预测 序列运算理论 极值负荷 出现时刻 |
Short Term Probabilistic Forecasting of the Magnitude and Timing of Extreme Load |
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| CHEN Xinyu,KANG Chongqing,CHEN Minjie,Haidian District,Beijing ,China,.Dept.of Electrical Engineering , Computer Science,Massachusetts Institute of Technology,Cambridge,MA,USA.Short Term Probabilistic Forecasting of the Magnitude and Timing of Extreme Load[J].Proceedings of the CSEE,2011,31(22). |
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| Authors: | CHEN Xinyu KANG Chongqing CHEN Minjie Haidian District Beijing China Deptof Electrical Engineering Computer Science Massachusetts Institute of Technology Cambridge MA USA |
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| Affiliation: | CHEN Xinyu1,KANG Chongqing1,CHEN Minjie2(1.State Key Lab of Control and Simulation of Power Systems and Generation Equipments(Dept.of Electrical Engineering,Tsinghua University),Haidian District,Beijing 100084,China,2.Dept.of Electrical Engineering and Computer Science,Massachusetts Institute of Technology,02139,Cambridge,MA,USA) |
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| Abstract: | As the foundation of system daily scheduling and operations,current deterministic forecasting algorithm of the magnitude and timing of extreme load is not satisfactory.Probabilistic forecasting is an effective way to reduce the risk of inaccurate forecasting of extreme load.This paper took peak load as an example,analyzed the multi sub-peaks characteristic of load curve,studied the statistical features of the peak load magnitude and timing,established the regression model between peak load occurrence time a... |
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| Keywords: | power systems short term forecasting bus load forecasting sequence operation theory extreme load occurrence time |
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