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

Autor:	M. O. W. Grand, WL Wil Kling, Phuong H. Nguyen, B.M.J. Vonk, J.G. Slootweg
Přispěvatelé:	Electrical Energy Systems, Cyber-Physical Systems Center Eindhoven
Rok vydání:	2012
Předmět:	Engineering Artificial neural network business.industry Load forecasting Mutual information Demand forecasting Energy storage Term (time) Reliability engineering Smart grid business Physics::Atmospheric and Oceanic Physics Simulation Selection (genetic algorithm)
Zdroj:	Proceedings of the 47th International Universities' Power Engineering Conference (UPEC 2012), 4-7 September 2012, 1-6 STARTPAGE=1;ENDPAGE=6;TITLE=Proceedings of the 47th International Universities' Power Engineering Conference (UPEC 2012), 4-7 September 2012
DOI:	10.1109/upec.2012.6398581
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d22937caf606c63241639c6d98c7d6db https://doi.org/10.1109/upec.2012.6398581 Zobrazit plný text záznamu