Customer perceived value theory and PSO-LightGBM algorithm-based approach to evaluating satisfaction factors with Net-zero energy building retrofits

Qin Xiaoxiang (), Yin Junjia (), Nuzul Azam Haron () and Nabilah Abu Bakar ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 3, 2508-2530

Abstract: With the growing global focus on sustainability, net-zero energy building (NZEB) retrofitting has become a meaningful way to improve energy efficiency and reduce carbon emissions. However, research on evaluating residents' satisfaction with these retrofit projects is lacking. This study aims to fill this gap using customer perceived value (CPV) theory and PSO-LightGBM algorithms to evaluate the factors influencing satisfaction with NZEB retrofits. The research framework is based on CPV, with functional, emotional, social, and cost values as critical drivers of satisfaction. Post-retrofit feedback was analyzed using PSO-LightGBM and other machine learning (ML) models like CatBoost, XGBoost, and AdaBoost. The study found that “government subsidies and support,” “living comfort,” “personalized experience,” “social acceptance,” and “improved environmental image” are the top five most important factors affecting satisfaction with renovating NZEB. In addition, the PSO-LightGBM algorithm excels in accuracy, precision, and F1 score, outperforming other ML models. The study also suggests several enhancement strategies, such as the use of energy-efficient technologies and environmentally friendly materials, to ensure that the performance of the retrofitted buildings improves significantly.

Keywords: Building retrofit; Customer perceived value; LightGBM; machine learning; Net-zero energy buildings; Particle swarm optimization (PSO). (search for similar items in EconPapers)
Date: 2025
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