摘要
电力市场中,价格作为各市场主体运营工作的重要参考信息,一直得到广泛的重视和研究。但是,电价影响因素之间复杂的相互作用增加了电价预测建模的难度。针对该问题,该文提出了一种基于独立分量分析-支持向量机的系统边际价格预测混合模型。首先,该模型基于影响因素的高阶统计信息,通过构造混合优化变换函数,建立自适应的独立分量分析迭代算法,并提出基于峭度的去冗余新方法,实现了电价影响因素的特征提取,挖掘出更具表征能力的电价有效影响因素集。然后,将该样本集用于回归支持向量机的训练,建立了独立分量分析与支持向量机相结合的电价预测模型。该模型充分发挥独立分量分析的特征提取优势,增强了支持向量机模型输入样本的表征能力,使电价预测模型更加准确。美国加州现货电能量市场的实例数据验证了该文所建模型的有效性。
Price forecasting has been a useful tool for market participants to provide important economic information. But the complicated influence factors of the price make the forecasting more difficult. So a novel hybrid model for forecasting system marginal price （SMP） in spot market is presented, which integrates independent component analysis with support vector machine, called ICA-SVM. First, this paper designs the ICA self-adapting iteration method by constructing hybrid optimal transform function, and a new de-redundancy function based on higher-order statistic information. Then the feature extraction of SMP influencing factors is realized, which produces SMP effective influencing factor sample set. After the training of regress SVM with the obtained sample set, SMP forecast model is built whose accuracy is enhanced with the generalization ability of support vector machine and the feature extraction ability of independent component analysis. Finally, real-word data of spot market in California is employed to demonstrate the validity of the proposed approach.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2005年第17期66-71,共6页
Proceedings of the CSEE
关键词
系统边际价格
独立分量分析
特征提取
支持向量机
System marginal price
Independent component analysis
Feature extraction
Support vector machine
