Developing a Data-Driven AI Model to Enhance Energy Efficiency in UK Residential Buildings

Abstract

Residential buildings contribute 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 EPC (Energy Performance Certificate) 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 (R2) 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. All 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.

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“…Consuegra, Ludueña, Frutos, Frutos, Alonso, and Oteiza [21] assess energy improvements achieved through building envelope retrofitting, emphasizing the challenge of quantifying energy savings accurately due to varying occupant behaviors. Seraj, Jahromi, and Amirkhani [22] develop a data-driven AI model for enhancing energy efficiency in UK residential buildings, highlighting the potential of machine learning in predicting energy performance under retrofit scenarios. Bahrami, Soltanifar, Fallahi, Meschi, and Sohani [23] explore the energy and economic advantages of using solar stills for renewable energy-based multi-generation, demonstrating the viability of solar desalination as a cost-effective alternative.…”