Seraj, Hamidreza, Bahadori-Jahromi, Ali ORCID: https://orcid.org/0000-0003-0405-7146 and Amirkhani, Shiva (2024) Developing a Data-Driven AI Model to Enhance Energy Efficiency in UK Residential Buildings. Sustainability, 16 (8).

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Abstract

Residential buildings contribute to 30% of the UK’s total final energy consumption. However, with less than one percent of its housing stock being replaced annually, retrofitting existing homes has significant importance in meeting energy efficiency targets. Consequently, many physics-based and data-driven models and tools have been developed to analyse the effects of retrofit strategies from various points of view. This paper aims to develop a data-driven AI model that predicts buildings’ energy performance based on their features under various retrofit scenarios. In this context, four different machine learning models were developed based on the Energy Performance Certificate (EPC) dataset for residential buildings and Standard Assessment Procedure (SAP) guidelines in the UK. Additionally, an interface was designed that enables users to analyse the effect of different retrofit strategies on a building’s energy performance using the developed AI models. The results of this study revealed the artificial neural network as the most accurate predictive model, with a coefficient of determination (R^2) of 0.82 and a mean percentage error of 11.9 percent. However, some conceptual irregularities were observed across all the models when dealing with different retrofit scenarios. In summary, such tools can be further improved to offer a potential alternative or support to physics-based models, enhancing the efficiency of retrofitting processes in buildings.

Keywords: machine learning, energy performance certificate, building energy consumption

Item Type:	Article
Identifier:	10.3390/su16083151
Keywords:	machine learning; energy performance certificate; building energy consumption
Subjects:	Construction and engineering
Depositing User:	Ali Bahadori-Jahromi
Date Deposited:	15 Apr 2024 11:19
Last Modified:	15 Apr 2024 11:19
URI:	https://repository.uwl.ac.uk/id/eprint/11426

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