摘要
电价序列由于受到多种因素的影响往往具有随机性和波动性的特点,准确的电价预测对电力市场优化运营有一定的指导意义。挖掘电价序列的波动模式是提高电价预测精确度的关键。为此将小波变换去噪与改进秩次集对方法相结合,建立了一种新的电价预测模型。首先,利用小波变换对原始电价序列数据进行去噪处理。其次,利用改进的秩序集对分析法对去噪后的电价序列进行预测。通过对美国PJM电力市场实际的电价序列进行仿真实验。结果表明,所提方法具有更好的预测效果,从而验证了模型的有效性。
Electricity price forecasting plays an important role in optimizing power market operation.However,electricity price series presents to be random and fluctuant due to the impact of a variety of factors.The key to improving the forecasting accuracy is to figure out the fluctuation pattern.Therefore,this paper proposed a new electricity price forecasting model by combining wavelet transform denoising and improved set pair analysis.Firstly,we used wavelet transform to denoise original electricity price sequence.Secondly,we applied the improved set pair analysis method in forecasting the denoised electricity price sequence.We conducted simulation experiment on the actual electricity price sequence of PJM power market in the United States.The experimental results show that the proposed model is effective in improving forecasting accuracy.
作者
王阳
刘鑫屏
WANG Yang;LIU Xinping(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)
出处
《华北电力大学学报（自然科学版）》
CAS
北大核心
2020年第4期29-35,共7页
Journal of North China Electric Power University：Natural Science Edition
基金
国家重点研发计划项目(2017YFB0902100).
关键词
小波变换
秩序集对分析
波动模式
电价预测
wavelet transform
set pair analysis
fluctuation pattern
electricity price forecasting
