Luo, X.J. and O. Oyedele, Lukumon Life cycle optimisation of building retrofitting considering climate change effects. Energy and Buildings Volume 258, 1 March 2022, 111830, 258.

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Abstract

Novelty Climate change has significant impacts on building energy performance. A novel life cycle optimisation strategy is developed for determining optimal retrofitting solutions for office buildings with climate change effects taken into consideration. The first innovation is that a hybrid genetic algorithm and artificial neural network model is developed to estimate future heating and electrical energy demands. The second innovation is that performance of integrated retrofitting measures under climate change conditions is evaluated. The third innovation is that life cycle cost optimisation is conducted using future weather profiles, while the energy usage and carbon footprint of the retrofitted building over its whole life span is evaluated. Methodology The proposed life cycle optimisation strategy is implemented on two campus buildings in Bristol, the United Kingdom. The historical weather profile and energy consumption data during the past two years is collected to develop and train the hybrid energy prediction model. The future weather profile, including air temperature, relative humidity, precipitation rate, solar radiation, wind speed and cloud percentage, is projected using the HadCM3 model. The collective performance of various passive, active and renewables retrofitting options is investigated. Major results and future application It is found that there exists a distinct discrepancy between optimal retrofitting solutions determined using the current and future weather conditions. Moreover, there would be at most 4.7% over-estimation or 54.7% under-estimation of lifetime cost, energy and carbon if the selected optimal retrofitting solution from current weather conditions is adopted under climate change conditions. Therefore, the proposed framework can provide a meaningful guideline in determining appropriate retrofitting solutions and supporting energy efficiency policies to achieve net-zero by 2050.

Item Type:	Article
Uncontrolled Keywords:	Climate change; Building retrofitting; Life cycle optimisation; Artificial neural network; Sustainability
Subjects:	English > Climate Change Adaptation
Depositing User:	Susanna Carlsten
Date Deposited:	11 May 2022 08:03
Last Modified:	11 May 2022 08:03
URI:	http://eprints.sparaochbevara.se/id/eprint/1162

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