Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter

Jun Dong; Shilin Nie

doi:doi:10.11648/j.epes.20190803.11

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Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter

Jun Dong, Shilin Nie

Published in American Journal of Electrical Power and Energy Systems (Volume 8, Issue 3)

Received: 16 May 2019 Accepted: 13 June 2019 Published: 26 June 2019

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Abstract

The customer baseline load is an important reference for the industrial and commercial users to participate in the demand response project, and is affected by various factors such as the environment and user electricity usage. In order to improve the accuracy of the baseline load forecasting of industrial and commercial users, a demand response baseline load forecasting model based on time series and Kalman filter combination is proposed. The marginal contribution rate of the single forecasting model to the combined model is obtained by the Shapley value method, then gets optimal prediction results. The case results show that the Kalman filter model has higher prediction accuracy in the period of stable load fluctuation, and the ARMA model has higher prediction accuracy in the period of large load fluctuation, and the combined prediction model combines the advantages of both models and reduces the single model is affected by the time factor in the prediction process, which improves the overall prediction accuracy and expands the scope of application.

Published in	American Journal of Electrical Power and Energy Systems (Volume 8, Issue 3)
DOI	10.11648/j.epes.20190803.11
Page(s)	71-76
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), 2019. Published by Science Publishing Group

Keywords

Baseline Load, Demand Response, Load Forecasting, ARMA, Kalman Filter

References

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[3]	SUN Jianlong, ZHOU Zongrong, TAN Jian, et al. Research on Calculation Method of User Baseline Load in Demand Response [J]. E-lectronic Technology Application, 2014, (z1): 202-205, 208.
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[7]	HU Yang, CHANG Xianrong. Short-term load forecasting based on improved EMD-PSVM [J]. Shaanxi Electric Power, 2016, 44 (03): 29-33.
[8]	QU Dapeng, WU Weihua, JIANG Di, et al. Calculation method of radial basis function neural network based on user demand side response baseline load [J]. ELECTRONIC TEST, 2014 (2X): 26-30.
[9]	LIU Guishun, YANG Jiaju, WANG Lei. Baseline load calculation with considering customer different electrical characterist-ics [J]. POWER DSM, 2016 (3): 17-22.
[10]	WANG Xiaofeng, SU Huilin, SONG L T, HUANG Q H. Differential Customer Baseline Load Forecasting Based on Load Subdivision [J]. Electric Power Engineering Technology, 2018, 37 (06): 33-38.
[11]	Grimm C, Energy D T E. Evaluating baselines for demand response programs [C]//AEIC Load Research Workshop. 2008.
[12]	Coughlin K, Piette M A, Goldman C, et al. Estimating demand response load impacts: Evaluation of baseline load models for non-residential buildings in California [J]. 2008.
[13]	Woo C K, Herter K. Residential demand response evaluation: a scoping study [J]. Energy and Environmental Economics, Inc. and Lawrence Berkeley National Laboratory, 2006.
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APA Style

Jun Dong, Shilin Nie. (2019). Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter. American Journal of Electrical Power and Energy Systems, 8(3), 71-76. https://doi.org/10.11648/j.epes.20190803.11

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ACS Style

Jun Dong; Shilin Nie. Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter. Am. J. Electr. Power Energy Syst. 2019, 8(3), 71-76. doi: 10.11648/j.epes.20190803.11

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AMA Style

Jun Dong, Shilin Nie. Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter. Am J Electr Power Energy Syst. 2019;8(3):71-76. doi: 10.11648/j.epes.20190803.11

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@article{10.11648/j.epes.20190803.11,
  author = {Jun Dong and Shilin Nie},
  title = {Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {8},
  number = {3},
  pages = {71-76},
  doi = {10.11648/j.epes.20190803.11},
  url = {https://doi.org/10.11648/j.epes.20190803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20190803.11},
  abstract = {The customer baseline load is an important reference for the industrial and commercial users to participate in the demand response project, and is affected by various factors such as the environment and user electricity usage. In order to improve the accuracy of the baseline load forecasting of industrial and commercial users, a demand response baseline load forecasting model based on time series and Kalman filter combination is proposed. The marginal contribution rate of the single forecasting model to the combined model is obtained by the Shapley value method, then gets optimal prediction results. The case results show that the Kalman filter model has higher prediction accuracy in the period of stable load fluctuation, and the ARMA model has higher prediction accuracy in the period of large load fluctuation, and the combined prediction model combines the advantages of both models and reduces the single model is affected by the time factor in the prediction process, which improves the overall prediction accuracy and expands the scope of application.},
 year = {2019}
}

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TY  - JOUR
T1  - Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter
AU  - Jun Dong
AU  - Shilin Nie
Y1  - 2019/06/26
PY  - 2019
N1  - https://doi.org/10.11648/j.epes.20190803.11
DO  - 10.11648/j.epes.20190803.11
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  - 71
EP  - 76
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20190803.11
AB  - The customer baseline load is an important reference for the industrial and commercial users to participate in the demand response project, and is affected by various factors such as the environment and user electricity usage. In order to improve the accuracy of the baseline load forecasting of industrial and commercial users, a demand response baseline load forecasting model based on time series and Kalman filter combination is proposed. The marginal contribution rate of the single forecasting model to the combined model is obtained by the Shapley value method, then gets optimal prediction results. The case results show that the Kalman filter model has higher prediction accuracy in the period of stable load fluctuation, and the ARMA model has higher prediction accuracy in the period of large load fluctuation, and the combined prediction model combines the advantages of both models and reduces the single model is affected by the time factor in the prediction process, which improves the overall prediction accuracy and expands the scope of application.
VL  - 8
IS  - 3
ER  -

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Author Information

Jun Dong

School of Economics and Management, North China Electric Power University, Beijing, China
Shilin Nie

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

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Plain Text BibTeX RIS

APA Style

Jun Dong, Shilin Nie. (2019). Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter. American Journal of Electrical Power and Energy Systems, 8(3), 71-76. https://doi.org/10.11648/j.epes.20190803.11

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ACS Style

Jun Dong; Shilin Nie. Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter. Am. J. Electr. Power Energy Syst. 2019, 8(3), 71-76. doi: 10.11648/j.epes.20190803.11

Copy | Download

AMA Style

Jun Dong, Shilin Nie. Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter. Am J Electr Power Energy Syst. 2019;8(3):71-76. doi: 10.11648/j.epes.20190803.11

Copy | Download

@article{10.11648/j.epes.20190803.11,
  author = {Jun Dong and Shilin Nie},
  title = {Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {8},
  number = {3},
  pages = {71-76},
  doi = {10.11648/j.epes.20190803.11},
  url = {https://doi.org/10.11648/j.epes.20190803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20190803.11},
  abstract = {The customer baseline load is an important reference for the industrial and commercial users to participate in the demand response project, and is affected by various factors such as the environment and user electricity usage. In order to improve the accuracy of the baseline load forecasting of industrial and commercial users, a demand response baseline load forecasting model based on time series and Kalman filter combination is proposed. The marginal contribution rate of the single forecasting model to the combined model is obtained by the Shapley value method, then gets optimal prediction results. The case results show that the Kalman filter model has higher prediction accuracy in the period of stable load fluctuation, and the ARMA model has higher prediction accuracy in the period of large load fluctuation, and the combined prediction model combines the advantages of both models and reduces the single model is affected by the time factor in the prediction process, which improves the overall prediction accuracy and expands the scope of application.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter
AU  - Jun Dong
AU  - Shilin Nie
Y1  - 2019/06/26
PY  - 2019
N1  - https://doi.org/10.11648/j.epes.20190803.11
DO  - 10.11648/j.epes.20190803.11
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  - 71
EP  - 76
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20190803.11
AB  - The customer baseline load is an important reference for the industrial and commercial users to participate in the demand response project, and is affected by various factors such as the environment and user electricity usage. In order to improve the accuracy of the baseline load forecasting of industrial and commercial users, a demand response baseline load forecasting model based on time series and Kalman filter combination is proposed. The marginal contribution rate of the single forecasting model to the combined model is obtained by the Shapley value method, then gets optimal prediction results. The case results show that the Kalman filter model has higher prediction accuracy in the period of stable load fluctuation, and the ARMA model has higher prediction accuracy in the period of large load fluctuation, and the combined prediction model combines the advantages of both models and reduces the single model is affected by the time factor in the prediction process, which improves the overall prediction accuracy and expands the scope of application.
VL  - 8
IS  - 3
ER  -

Copy | Download