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Short-Term Electricity Price Forecasting Based on Grey Prediction GM(1,3) and Wavelet Neural Network

Received: 30 July 2018     Accepted: 13 August 2018     Published: 4 September 2018
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Abstract

Electricity price is a core index that reflects the operation status of the power market, evaluates the efficiency of market competition, and is the basis for decision-making in the electricity market. Electricity price forecasting is of great significance to guide investment, allocate market resources spontaneously, achieve a basic balance of power supply and demand, and meet various service goals. In this paper, a short-term electricity price forecasting method based on a grey forecasting GM(1,3) and wavelet neural network combination model is adopted. Firstly, the power price sequence is decomposed and reconstructed by using the famous MALLAT algorithm of multi-resolution analysis based on wavelet transform theory, and then the final predictive electricity price sequence is obtained by using the BP neural network model. Then the predicted electricity price sequence is used as a relevant factor affecting the future daily electricity price and input to the grey GM(1,3) forecasting model for electricity price forecasting to obtain the final forecasting result. The model training and forecasting based on the 2012 load and price data published by the PJM power market in the United States show that the prediction model established by this method has higher prediction accuracy. Thus, it has important research significance for electricity market price forecasting.

Published in American Journal of Electrical Power and Energy Systems (Volume 7, Issue 4)
DOI 10.11648/j.epes.20180704.12
Page(s) 50-55
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Grey Prediction, Wavelet Neural Network, Electricity Price Forecasting Combination Model

References
[1] Cheng Yuye. Research on short-term power load forecast based on artificial neural network [D]. Zhejiang University, 2017.
[2] Zou Zhiguo. Research on multi-component signal analysis method based on empirical mode decomposition[D]. Harbin Institute of Technology, 2016.
[3] GUO Xinxin. Short-term electricity price combination prediction based on EEMD and wavelet neural network[J]. Journal of Chongqing Technology and Business University, 2016, 33(02):21-25.
[4] Wang Huizhong, Zhou Jia, Liu Yu. A review of short-term load forecasting methods for power systems [J]. Electric Automation, 2015, 37(01): 1-3+39.
[5] Shen Xiaoling. Analysis of Factors Affecting Short-Term Load Forecasting of Power System [J]. Science & Technology Information, 2011(05): 773-774.
[6] Wang Ruiqing, Li Yuzeng. Short-term electricity price forecasting using particle swarm optimization GM(1,2) with error correction [J]. Power System Protection and Control, 2011, 39(13): 41-45+52.
[7] Su Juan, Du Songhuai. Grey forecasting model of GM(1,2) short-term spot electricity price [J]. Relay, 2006(01):46-49.
[8] Jiang Jinan. Short-term power load forecasting based on wavelet denoising and artificial neural network [D]. Guangxi University, 2013.
[9] Liu Yusheng. Application of PSO-optimized GM(1,1) power-neural network model in short-term electricity price forecast [D]. Lanzhou University, 2011.
[10] YAMIN H Y, SHAH I S, LI Z. Adaptive Short-term Electricity Price Forecasting Using Artificial Neural Networks in the Restructured Power Markets [J].Electrical Power and Energy Systems, 2004, 26( 8): 571-581
[11] WU Xinghua, ZHOU Hui. Short-term electricity price forecast based on subtraction clustering and adaptive fuzzy neural network [J]. Power System Technology, 2007 (19): 69-73.
[12] Chen Sijie. Application of wavelet neural network in power system short-term electricity price forecast [D]. Zhejiang University, 2006.
[13] Wang Meng, Jing Zhibin, Sun Bing, Liang Zhenfei. Prediction of short-term market clearing price based on BP neural network [J]. China Electric Power Education, 2011(30):100-102.
[14] WU Z, HUANG N E. Ensemble Empirical Mode Decomposition: A Noise-assisted Data Analysis Method [J]. Advances in Adaptive Data Analysis, 2009(1): 1-41.
Cite This Article
  • APA Style

    Dong Jun, Yang Peiwen. (2018). Short-Term Electricity Price Forecasting Based on Grey Prediction GM(1,3) and Wavelet Neural Network. American Journal of Electrical Power and Energy Systems, 7(4), 50-55. https://doi.org/10.11648/j.epes.20180704.12

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    ACS Style

    Dong Jun; Yang Peiwen. Short-Term Electricity Price Forecasting Based on Grey Prediction GM(1,3) and Wavelet Neural Network. Am. J. Electr. Power Energy Syst. 2018, 7(4), 50-55. doi: 10.11648/j.epes.20180704.12

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    AMA Style

    Dong Jun, Yang Peiwen. Short-Term Electricity Price Forecasting Based on Grey Prediction GM(1,3) and Wavelet Neural Network. Am J Electr Power Energy Syst. 2018;7(4):50-55. doi: 10.11648/j.epes.20180704.12

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  • @article{10.11648/j.epes.20180704.12,
      author = {Dong Jun and Yang Peiwen},
      title = {Short-Term Electricity Price Forecasting Based on Grey Prediction GM(1,3) and Wavelet Neural Network},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {7},
      number = {4},
      pages = {50-55},
      doi = {10.11648/j.epes.20180704.12},
      url = {https://doi.org/10.11648/j.epes.20180704.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20180704.12},
      abstract = {Electricity price is a core index that reflects the operation status of the power market, evaluates the efficiency of market competition, and is the basis for decision-making in the electricity market. Electricity price forecasting is of great significance to guide investment, allocate market resources spontaneously, achieve a basic balance of power supply and demand, and meet various service goals. In this paper, a short-term electricity price forecasting method based on a grey forecasting GM(1,3) and wavelet neural network combination model is adopted. Firstly, the power price sequence is decomposed and reconstructed by using the famous MALLAT algorithm of multi-resolution analysis based on wavelet transform theory, and then the final predictive electricity price sequence is obtained by using the BP neural network model. Then the predicted electricity price sequence is used as a relevant factor affecting the future daily electricity price and input to the grey GM(1,3) forecasting model for electricity price forecasting to obtain the final forecasting result. The model training and forecasting based on the 2012 load and price data published by the PJM power market in the United States show that the prediction model established by this method has higher prediction accuracy. Thus, it has important research significance for electricity market price forecasting.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Short-Term Electricity Price Forecasting Based on Grey Prediction GM(1,3) and Wavelet Neural Network
    AU  - Dong Jun
    AU  - Yang Peiwen
    Y1  - 2018/09/04
    PY  - 2018
    N1  - https://doi.org/10.11648/j.epes.20180704.12
    DO  - 10.11648/j.epes.20180704.12
    T2  - American Journal of Electrical Power and Energy Systems
    JF  - American Journal of Electrical Power and Energy Systems
    JO  - American Journal of Electrical Power and Energy Systems
    SP  - 50
    EP  - 55
    PB  - Science Publishing Group
    SN  - 2326-9200
    UR  - https://doi.org/10.11648/j.epes.20180704.12
    AB  - Electricity price is a core index that reflects the operation status of the power market, evaluates the efficiency of market competition, and is the basis for decision-making in the electricity market. Electricity price forecasting is of great significance to guide investment, allocate market resources spontaneously, achieve a basic balance of power supply and demand, and meet various service goals. In this paper, a short-term electricity price forecasting method based on a grey forecasting GM(1,3) and wavelet neural network combination model is adopted. Firstly, the power price sequence is decomposed and reconstructed by using the famous MALLAT algorithm of multi-resolution analysis based on wavelet transform theory, and then the final predictive electricity price sequence is obtained by using the BP neural network model. Then the predicted electricity price sequence is used as a relevant factor affecting the future daily electricity price and input to the grey GM(1,3) forecasting model for electricity price forecasting to obtain the final forecasting result. The model training and forecasting based on the 2012 load and price data published by the PJM power market in the United States show that the prediction model established by this method has higher prediction accuracy. Thus, it has important research significance for electricity market price forecasting.
    VL  - 7
    IS  - 4
    ER  - 

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Author Information
  • Department of Economics and Management, North China Electric Power University, Beijing, China

  • Department of Economics and Management, North China Electric Power University, Beijing, China

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