An ANN-based energy forecasting framework for the district level smart grids

Yuce, Baris ORCID: https://orcid.org/0000-0002-9937-1535, Mourshed, Monjur ORCID: https://orcid.org/0000-0001-8347-1366 and Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 2017. An ANN-based energy forecasting framework for the district level smart grids. Hu, Jia, Leung, Victor C. M., Yang, Kun, Zhang, Yan, Gao, Jianliang and Yang, Shusen, eds. Smart Grid Inspired Future Technologies, Vol. 175. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, [Smart Grid Inspired Future Technologies]. Springer International Publishing, pp. 107-117. (10.1007/978-3-319-47729-9_12)

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Abstract

This study presents an Artificial Neural Network (ANN) based district level smart grid forecasting framework for predicting both aggregated and disaggregated electricity demand from consumers, developed for use in a low-voltage smart electricity grid. To generate the proposed framework, several experimental studies have been conducted to determine the best performing ANN. The framework was tested on a microgrid, comprising six buildings with different occupancy patterns. Results suggested an average percentage accuracy of about 96%, illustrating the suitability of the framework for implementation

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	Springer International Publishing
ISBN:	9783319477282
Funders:	European Commission
Last Modified:	02 Nov 2022 10:01
URI:	https://orca.cardiff.ac.uk/id/eprint/97195

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