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 2016. An ANN-based energy forecasting framework for the district level smart grids. Presented at: SmartGIFT 2016 - 1st EAI International Conference on Smart Grid Inspired Future Technologies, Liverpool, UK, 19-20 May 2016. EAI,

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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 micro grid, 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:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords:	ANN, District Energy Management, Grid Electricity, Smart City
Publisher:	EAI
Date of Acceptance:	23 June 2016
Last Modified:	19 Nov 2022 08:58
URI:	https://orca.cardiff.ac.uk/id/eprint/93433

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