# [1]廖晓辉,周冰,杨冬强,等.一种基于HHT的短期电价组合预测方法[J].郑州大学学报(工学版),2016,37(01):10-14.[doi:10.3969/j.issn.1671-6833.201503041] 　Liao Xiaohui,Zhou Bing,Yang Dongqiang,et al.A Method for Short-term Electricity Price Forecasting Based on HHT[J].Journal of Zhengzhou University (Engineering Science),2016,37(01):10-14.[doi:10.3969/j.issn.1671-6833.201503041] 点击复制 一种基于HHT的短期电价组合预测方法() 分享到： var jiathis_config = { data_track_clickback: true };

37卷

2016年01期

10-14

2016-02-28

## 文章信息/Info

Title:
A Method for Short-term Electricity Price Forecasting Based on HHT

1.郑州大学 电气工程学院,河南 郑州,450001;2.郑州市供电公司,河南 郑州,450051
Author(s):
1. School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, 450001; 2. Zhengzhou Power Supply Company, Zhengzhou, Henan, 450051

Keywords:
DOI:
10.3969/j.issn.1671-6833.201503041

A

Abstract:
Short-term electricity price forecasting guarantees the maximum benefit of the parties involved in the power market. In view of the fact that the market clearing price has strong randomness and volatility, the paper proposes a combination forecasting model based on Hilbert-Huang transform. The price sequence is decom-posed into a number of intrinsic mode function components and the remainder by using the empirical mode de-composition theory. Different models were built for each intrinsic mode function according to the size of each component’ s average instantaneous frequency. Then the prediction results of each component are added up to obtain the final prediction value. And the model uses the actual data of PJM power market in the United States to test. Compared to the prediction results of any one sole model, this method accuracy were higher than single forecasting model, the maximum absolute error is 1. 53 S|/MWh and the mean absolute percentage error is 1. 61.