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

Dong Jun; Yang Peiwen

doi:doi:10.11648/j.epes.20180704.12

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

Dong Jun, Yang Peiwen

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.

DOI	10.11648/j.epes.20180704.12
Published in	American Journal of Electrical Power and Energy Systems (Volume 7, Issue 4, July 2018)
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), 2024. Published by Science Publishing Group

Keywords

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

References

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[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.
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[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.

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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

Dong Jun

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

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

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Plain Text BibTeX RIS

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}
}

Copy | Download

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  -

Copy | Download