Assessing the effect of people's behaviour on energy consumption in buildings

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Salim, S, 2022. Assessing the effect of people's behaviour on energy consumption in buildings. PhD, Nottingham Trent University.

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Abstract

Energy consumption in the residential sector has become increasingly important. This is all the more significant considering the pandemic, when people spent more time at home. In order to achieve the zero-carbon target of the Paris Agreement of which the UK is a part, there is a drive to insulate buildings, on the assumption that the more insulated a building is, the more efficient it will perform. In this study, the author examines the effect of peoples' behaviour, particularly window opening, as a behavioural pattern of the occupants and examines impact of occupant behaviour on the energy consumption of residential buildings in the UK. To identify the key factors that influence occupant behaviour, thermal imaging of residential buildings across Nottingham was done, followed by survey with questions regarding window opening behaviour of the participants. This was followed by the analysis of energy usage in Social Housing. Temperature data collected for a period of 14 months, from 17 houses, were analysed and the energy demand was calculated. Findings show that energy efficiency of a building holds an explicate relationship with the behaviour of occupants in the buildings, regardless of the building insulation properties. A highly insulated building could consume as much energy as a badly insulated house, due to people's behaviour. There was empirical evidence that for a well-insulated house with window open, the heating time increases by a factor of 1.6 when compared to similar insulated house with window closed. Hence the assumption that the more the insulation, the more the energy efficiency, might not be true. Findings also show that people's behaviour could reduce the effect of insulation. So, what theoretically is a well-insulated building might behave like a badly insulated house in terms of energy efficiency, depending on the behaviour of occupants. Thermal imaging is a helpful tool in visualising the impact of window opening and can be used to make occupants aware of the effect of their behaviour. ANN feed forward neural network model to predict the window opening behaviour based on the room temperature, radiator temperature and outside ambient temperature was developed. Energy costs of highly insulated and window open house defers from that of badly insulated and window open house only by 2%, while there is a difference of 10% between highly insulated and window open house and highly insulated and window closed house, showing that a well insulated house with window open behaves in a similar way to a poorly insulated house. ANN feed forward neural network model to predict the window opening behaviour based on the room temperature, radiator temperature and outside ambient temperature was developed. The model predicted window opening with 98.8% accuracy for well insulated window closed house and 92% accuracy for well insulated window open house. The findings suggest that people's behaviour could reduce the effect of insulation in residential buildings.

Item Type:	Thesis
Creators:	Salim, S.
Date:	April 2022
Rights:	This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, University, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed to the author of the Intellectual Property Rights.
Divisions:	Schools > School of Architecture, Design and the Built Environment
Record created by:	Linda Sullivan
Date Added:	20 Dec 2022 10:08
Last Modified:	20 Dec 2022 10:08
URI:	https://irep.ntu.ac.uk/id/eprint/47683