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Authors: Yaqub Rafiq 1 ; Shen Wei 1 ; Robert Guest 2 ; Robert Stone 3 and Pieter de Wilde 1

Affiliations: 1 Plymouth University, United Kingdom ; 2 University of Birmingham, United Kingdom ; 3 Birmingham University, United Kingdom

Keyword(s): Artificial Neural Networks, Building Simulation, Building Energy

Abstract: In this paper Artificial Neural Networks (ANNs) is used to model effects of various human behaviour on energy consumption of the residential buildings in the UK. A virtual model of a bungalow has been developed in which various aspects of the, physical changes in the building such as wall and floor insulation, single and double glazing combined by the human behaviour aspects such as thermostat setting, various periods of door and/or window opening etc. are modelled using EnergyPlus software for evaluating energy consumption for a combination of scenarios. ANNs were then used to learn the effects of various human behaviours on energy consumption. The results demonstrated that the ANN is capable of learning the effects that changes in the human behaviour have in evaluating energy saving in residential buildings and it generated results very quickly for unseen cases.

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Paper citation in several formats:
Rafiq, Y.; Wei, S.; Guest, R.; Stone, R. and de Wilde, P. (2014). Applying Artificial Neural Networks to Promote Behaviour Change for Saving Residential Energy. In Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2014) - ANNIIP; ISBN 978-989-758-041-3, SciTePress, pages 3-10. DOI: 10.5220/0005049600030010

@conference{anniip14,
author={Yaqub Rafiq. and Shen Wei. and Robert Guest. and Robert Stone. and Pieter {de Wilde}.},
title={Applying Artificial Neural Networks to Promote Behaviour Change for Saving Residential Energy},
booktitle={Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2014) - ANNIIP},
year={2014},
pages={3-10},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005049600030010},
isbn={978-989-758-041-3},
}

TY - CONF

JO - Proceedings of the International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2014) - ANNIIP
TI - Applying Artificial Neural Networks to Promote Behaviour Change for Saving Residential Energy
SN - 978-989-758-041-3
AU - Rafiq, Y.
AU - Wei, S.
AU - Guest, R.
AU - Stone, R.
AU - de Wilde, P.
PY - 2014
SP - 3
EP - 10
DO - 10.5220/0005049600030010
PB - SciTePress

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