Improving short-term load forecasting for a local energy storage system

B.M.J. Vonk; H.P. Nguyen; M.O.W. Grond; J.G. Slootweg; W.L. Kling

doi:10.1109/UPEC.2012.6398581

Improving short-term load forecasting for a local energy storage system

B.M.J. Vonk, H.P. Nguyen, M.O.W. Grond, J.G. Slootweg, W.L. Kling

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic

13 Citations (Scopus)

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Abstract

Short-term load forecasting is a crucial step for proper operation of a battery energy storage system. In this paper, an artificial neural network forecaster is used for hourly based forecasting of the distributed power generation and load consumption. This paper focusses on using mutual information for the selection of training data for the artificial neural network models of the forecaster. The proposed approach reduces the forecasting error, especially after transients in the input-output mapping. Simulations with real data sets are executed to verify the effectiveness of the method.

Original language	English
Title of host publication	Proceedings of the 47th International Universities' Power Engineering Conference (UPEC 2012), 4-7 September 2012
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-6
ISBN (Print)	978-1-4673-2855-5
DOIs	https://doi.org/10.1109/UPEC.2012.6398581
Publication status	Published - 2012

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Vonk, B. M. J., Nguyen, H. P., Grond, M. O. W., Slootweg, J. G., & Kling, W. L. (2012). Improving short-term load forecasting for a local energy storage system. In Proceedings of the 47th International Universities' Power Engineering Conference (UPEC 2012), 4-7 September 2012 (pp. 1-6). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/UPEC.2012.6398581

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abstract = "Short-term load forecasting is a crucial step for proper operation of a battery energy storage system. In this paper, an artificial neural network forecaster is used for hourly based forecasting of the distributed power generation and load consumption. This paper focusses on using mutual information for the selection of training data for the artificial neural network models of the forecaster. The proposed approach reduces the forecasting error, especially after transients in the input-output mapping. Simulations with real data sets are executed to verify the effectiveness of the method.",

author = "B.M.J. Vonk and H.P. Nguyen and M.O.W. Grond and J.G. Slootweg and W.L. Kling",

year = "2012",

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Improving short-term load forecasting for a local energy storage system. / Vonk, B.M.J.; Nguyen, H.P.; Grond, M.O.W. et al.
Proceedings of the 47th International Universities' Power Engineering Conference (UPEC 2012), 4-7 September 2012. Piscataway: Institute of Electrical and Electronics Engineers, 2012. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic

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AU - Kling, W.L.

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AB - Short-term load forecasting is a crucial step for proper operation of a battery energy storage system. In this paper, an artificial neural network forecaster is used for hourly based forecasting of the distributed power generation and load consumption. This paper focusses on using mutual information for the selection of training data for the artificial neural network models of the forecaster. The proposed approach reduces the forecasting error, especially after transients in the input-output mapping. Simulations with real data sets are executed to verify the effectiveness of the method.

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M3 - Conference contribution

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BT - Proceedings of the 47th International Universities' Power Engineering Conference (UPEC 2012), 4-7 September 2012

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Improving short-term load forecasting for a local energy storage system

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