An ANN-Based Energy Forecasting Framework for the District Level Smart Grids

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 175)


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 study 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.


ANN District energy management Grid electricity Smart city 



The authors would like to acknowledge the financial support of the European Commission in the context of the MAS2TERING project (Ref: 619682) funded under the ICT-2013.6.1 - Smart Energy Grids program.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.School of Engineering, BRE Centre for Sustainable EngineeringCardiff UniversityCardiffUK

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