Short-Term Power Load Forecasting Based on EMD-Grey Model
American Journal of Electrical Power and Energy Systems
Volume 7, Issue 4, July 2018, Pages: 42-49
Received: Jul. 30, 2018;
Accepted: Aug. 14, 2018;
Published: Sep. 4, 2018
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Dong Jun, School of Economics and Management, North China Electric Power University, Beijing, China
Wang Pei, School of Economics and Management, North China Electric Power University, Beijing, China
Dou Xihao, School of Economics and Management, North China Electric Power University, Beijing, China
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With the issuance of "electricity reform No. 9 document" in 2015, a new round of power system reform in China has been continuously pushed forward. With the gradual development of the pilot spot market in various provinces, the importance of load forecasting to the various main bodies of the spot power market has been constantly revealed. In order to improve the accuracy of short-term load forecasting in the spot market, and better highlight the randomness, periodicity and related trend of load fluctuation, this paper proposes a short-term load forecasting based on grey model and the EMD combination model, predict the future 24-hour load. In other words, GM(1,1) is used to predict the residual value sequence of EMD decomposition. In order to ensure the stability of the residual value sequence, improve the accuracy of the prediction and improve the effect of short-term load forecasting. Combined with MATLAB tools, the combined prediction model was simulated and verified by using the America PJM power market load data. The comparison results of the combined model with the single GM(1,1) and GM(1,2) respectively show that the combined model can significantly improve the accuracy of load forecasting compared with the traditional grey model method, providing the method guidance for load forecasting to better participate in the demand response under the new market environment.
Load Forecasting, EMD, Grey Model, Combination Model
To cite this article
Short-Term Power Load Forecasting Based on EMD-Grey Model, American Journal of Electrical Power and Energy Systems.
Vol. 7, No. 4,
2018, pp. 42-49.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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Fei Xi. Short-term load prediction of power system based on linear time series mathematical model [J]. China high-tech zone, 2018(14).
Yuan S, Yang L, Shang B, Li X, Zhang H. Short term power network gateway load forecasting algorithm based on ARMR model[C]// Information Technology, Networking, Electronic and Automation Control Conference, IEEE. IEEE, 2016: 497-501.
Tan Fenglei, Zhang Zhaojun, Zhu Chao, Zhang jun. Study on an improved exponential smoothing load forecasting method [J]. Power demand side management, 2016, 18(6): 22-26.
Xie Beimin, Zhao Xuesong. Study on power load prediction method based on improved wavelet analysis [J]. Science and technology innovation and application, 2016(36): 207-207.
Xie min, Deng Jialiang, Ji Xiang, Liu mingbo. A method to predict the cooling load of support vector machines based on the optimization of information entropy and variable precision rough set [J]. Grid technology, 2017, 41(1): 210-214.
Cheng Chao. Study on improved forecasting method of monthly power sales based on time series method and regression analysis [D]. Chongqing university, 2016.
Silva PRN, Carvalho AP, Gabbar HA, Vieira P, Costa CT. Fault Diagnosis in Transmission Lines Based on Leakage Current and Qualitative Trend Analysis[C]// International Conference on Promising Electronic Technologies. 2017: 87-92.
Li Long, Wei Jing, Li Canbing, Cao Yijia, Song Junying, Fang Baling. Load model prediction based on artificial neural network [J]. Acta Electrotechnical Sinica, 2015, 30(8): 225-230.
Zhu Xuexiong. Forecasting research of short-term residential load based on artificial neural network [J]. Science and technology innovation and application, 2017(23): 0-20.
Zhao Haiqing. Grey model based on accumulation method and its application in power load prediction [J]. China electric power, 2016(s1): 94-95.
Zhang Bing, Zhou Buxiang, Shi Min, Wei Jinxiao. Short-term load prediction based on grey correlation analysis and random forest regression model [J]. Hydro-electric energy science, 2017(4): 203-207.
Li Chuntao, Li Xiaocong, Yuan Hui, Qui Hao, Luo Hongliang. Short-term load forecasting based on improved grey model [J]. electric switch, 2017, 55 (2): 11-13.
Deng Ao, Jin Min. Emd-based time scale feature extraction and its application in short-term power load prediction [J]. Computer application research, 2018(10): 1-6.
Liu Xiaozhe. Research on power load forecasting based on EMD-Grey Markov model [D]. HeFei University of Technology, 2013.
Huang N E. New method for nonlinear and nonstationary time series analysis: empirical mode decomposition and Hilbert spectral analysis [C]// Proc of SPIE: The International Society for Optical Engineering, 2000: 197-209.