Volume 8, Issue 3, May 2019, Page: 71-76
Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter
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
Received: May 16, 2019;       Accepted: Jun. 13, 2019;       Published: Jun. 26, 2019
DOI: 10.11648/j.epes.20190803.11      View  115      Downloads  26
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.
Keywords
Baseline Load, Demand Response, Load Forecasting, ARMA, Kalman Filter
To cite this article
Jun Dong, Shilin Nie, Demand Response Baseline Load Forecasting Based on the Combination of Time Series and Kalman Filter, American Journal of Electrical Power and Energy Systems. Vol. 8, No. 3, 2019, pp. 71-76. doi: 10.11648/j.epes.20190803.11
Copyright
Copyright © 2019 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/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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