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

Yuce, Baris, Mourshed, Monjur and Rezgui, Yacine 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: Publication
Status: Published
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
Last Modified: 31 Oct 2017 12:59
URI: http://orca-mwe.cf.ac.uk/id/eprint/93433

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