基于小波变换的系统边际电价分析与预测
Analysis and Forecasting SMP Using Wavelet Transform
作者：唐明(四川大学 水利水电学院，四川 成都 610065)；马光文(四川大学 水利水电学院，四川 成都 610065)；徐刚(四川大学 水利水电学院，四川 成都 610065)
Author：(School of Water Resources and Hydropower, Sichuan Univ., Chengdu 610065, China)；(School of Water Resources and Hydropower, Sichuan Univ., Chengdu 610065, China)；(School of Water Resources and Hydropower, Sichuan Univ., Chengdu 610065, China)
收稿日期：2006-11-30 年卷（期）页码：2007,39(4):12-15
期刊名称：工程科学与技术
Journal Name：Advanced Engineering Sciences
关键字：系统边际电价;预测;小波分析;人工神经网络
Key words：SMP; forecast; wavelet transform; ANN
基金项目：国家自然科学基金重点资助项目（50539140）
中文摘要
摘要：基于小波变换的系统边际电价(System Marginal Price，SMP)数据分析，根据系统边际电价的特点，建立用于系统边际电价预测的模型。利用小波变换时频局部化功能，将原电价时间序列分解成不同的尺度，对不同尺度上的子序列分别采用人工神经网络和AR模型进行预测，最后将不同尺度预测结果通过小波重构还原，得到系统边际电价预测结果。实例验证表明预测模型能有效提高预测精度，可用于系统边际电价预测。
英文摘要
sing wavelet transform, the characteristics of periodicity SMP time series have been probed. According to the characteristics of SMP, this paper presents combined forecast method based on wavelet transform. The non stationary time series of SMP is decompo sed into several detailed stationary time series and a smoothed non stationary time series according to the principle of wavelet decomposition. The stationary time series is simulated by using AR(p) method and the non stationary series is simulated by using artificial neural network model. The comparison shows that the error of the simulation adopting this method is smaller than that by using auto-regressive method.
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