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

Author

Listed:
  • Hamidreza Seraj

    (Department of Civil and Environmental Engineering, School of Computing and Engineering, University of West London, London W5 5RF, UK)

  • Ali Bahadori-Jahromi

    (Department of Civil and Environmental Engineering, School of Computing and Engineering, University of West London, London W5 5RF, UK)

  • Shiva Amirkhani

    (Sustainability and Climate Change, WSP, 6 Devonshire Square, London EC2M 4YE, UK)

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 (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. 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.

Suggested Citation

  • Hamidreza Seraj & Ali Bahadori-Jahromi & Shiva Amirkhani, 2024. "Developing a Data-Driven AI Model to Enhance Energy Efficiency in UK Residential Buildings," Sustainability, MDPI, vol. 16(8), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3151-:d:1372973
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    References listed on IDEAS

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    1. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
    2. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    3. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    4. Seyedzadeh, Saleh & Pour Rahimian, Farzad & Oliver, Stephen & Rodriguez, Sergio & Glesk, Ivan, 2020. "Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making," Applied Energy, Elsevier, vol. 279(C).
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