电价分布及分类预测模型  

Electricity Price Distribution and Classified Forecasting Model

作　　者：冯长有[1] 王锡凡[1] 王秀丽[1] 王文博[1] 

机构地区：[1]西安交通大学电气工程学院,陕西省西安市710049

出　　处：《电力系统自动化》2009年第6期25-30,共6页Automation of Electric Power Systems

基　　金：国家重点基础研究发展计划(973计划)资助项目(2004CB217905)~~

摘　　要：准确的电价预测可为各市场主体的运营、发展规划提供指导,降低电价波动带来的风险,文中提出了相关预测模型。首先,基于历史数据分析了负荷水平、供给功率、可调度负荷水平、与相邻区域的功率交换水平及时段等因素对电价分布的影响,并引入基准电价概念将电价分为正常电价和高电价;然后,以上述因素为输入变量,采用邻近点技术和支撑向量机(SVM)技术确定未来电价的类别归属,正常电价利用时间序列法预测,高电价则根据历史高电价信息加权估计得到。模型以电价分布为着眼点进行分类预测,降低了对时间的依赖程度,不仅可用于短期电价预测,也为中长期预测提供了有效思路。以澳大利亚市场Queensland地区的周电价预测为例说明其有效性和实用性,给出了预测和分类精度,并通过灵敏度分析研究了基准电价选取对模型分类精度的影响。This paper proposes a novel price forecasting model, which can help market participants operate effectively, make their development planning and lower risks associated with price uncertainty in a competitive power market. Firstly, based on historical market information, the impacts of some major factors on price distribution are analyzed in detail. These mainly include demand load, power supply, dispatchable load, power change with neighboring grid and corresponding period. A base price value is introduced to split electricity price into normal and high price parts. Then, the proximal point and support vector machine （SVM） techniques are used to classify future prices, which will be forecasted by different methods. If one future price is judged to be a normal one, it will be estimated using traditional time serial methods, otherwise it will be estimated using weighted historical high price data. The proposed model reduces the price dependence on time by classified forecasting, which is based on price distribution. It can be applied to short-term forecasting, and in the mean time, provides useful information for medium- and long-term price forecasting as well. Finally, the actual market data of Queensland in Australia power market are used to testify its validity and rationality. Moreover, the sensitivity of the base price is analyzed to investigate its influence on classification precision of the presented scheme.

关 键 词：电价分布 高电价 邻近点 支撑向量机 分类精度 电价预测 

分 类 号：TM73[电气工程—电力系统及自动化] F123.9[经济管理—世界经济]