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分时段短期电价预测
引用本文:张显,王锡凡,陈芳华,叶斌,陈皓勇.分时段短期电价预测[J].中国电机工程学报,2005,25(15):1-6.
作者姓名:张显  王锡凡  陈芳华  叶斌  陈皓勇
作者单位:1. 西安交通大学电力工程系,陕西省,西安市,710049
2. 安徽省电力设计院,安徽省,合肥市,230022
基金项目:基金项目:国家重点基础研究专项经费项目(2004CB217905);国家社会科学基金项目(04CJL0120).
摘    要:分时段电价序列比顺序电价序列的变化特征更单一,有利于电价的分析建模,从而提高预测精度,因此采用各时段电价分别预测的分时段预测方法。该文将相关系数作为选取电价影响因素的标准,考虑了历史电价、负荷、负荷率等影响电价的因素。以小波分析和神经网络作为工具,对不同输入因素和不同预测方法下的电价预测精度进行了研究,并重点比较了基于分时段电价序列的预测方法和基于顺序电价序列的预测方法。算例采用美国新英格兰电力市场历史数据,对其2002年第4季度的电价进行了连续预测。与基于顺序电价序列的预测方法相比,分时段短期电价预测方法能够使平均相对百分比误差下降约3个百分点。

关 键 词:电力市场  电价预测  分时段电价序列  顺序电价序列  小波分析  神经网络
文章编号:0258-8013(2005)15-0001-06
收稿时间:2005-02-08
修稿时间:2005年2月8日

SHORT-TERM ELECTRICITY PRICE FORECASTING BASED ON PERIOD-DECOUPLED PRICE SEQUENCE
ZHANG Xian,WANG Xi-fan,CHEN Fang-hua,YE Bin,CHEN Hao-yong.SHORT-TERM ELECTRICITY PRICE FORECASTING BASED ON PERIOD-DECOUPLED PRICE SEQUENCE[J].Proceedings of the CSEE,2005,25(15):1-6.
Authors:ZHANG Xian  WANG Xi-fan  CHEN Fang-hua  YE Bin  CHEN Hao-yong
Abstract:The paper presents a period-de, coupled price forecasting method. The period-decoupled price sequence has simpler features compared with the chronological price sequence, therefore it is more suitable to be analyzed and modeled. The correlation coefficient with electricity price sequence is determined as the standard to select the influence factors. System load rate is selected to be an important factor in price forecasting, which is the ratio of the system load to the available system capacity, and indicates the relation of supply and demand. The wavelet analysis and neural network are used as the price forecasting tool. The accuracy of price forecasting with different influence factors and different neural networks is studied. Furthermore, the forecasting methods based on the period-decoupled price sequence and the chronological price sequence are compared. The historical data from New England market is used in the case study to forecast the day-ahead system marginal price in the fourth quarter of 2002 continuously. The numerical results show that the forecasting method based on the period-decoupled price sequence can decrease mean absolute percentage error upto 3 percent compared with the forecasting method based on chronological price sequence.
Keywords:Power market  Price forecasting  Period-decoupled price sequence  Chronological price sequence  Wavelet analysis  Neural network
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