Application of machine learning in energy efficiency benchmarking of buildings: bibliometric and systematic review
DOI:
https://doi.org/10.46421/encacelacac.v18i1.7216Keywords:
Energy efficiency, Buildings, Benchmarking, Machine learningAbstract
This study aims to analyze the application of machine learning in energy efficiency benchmarking for buildings. Using the ProKnow-C method, the research involved selecting articles published between 2015 and 2024, resulting in a bibliometric analysis of 229 publications. Then, 47 articles detailing benchmarking stages using machine learning were selected and subjected to systematic analysis and research gap assessment. The analysis revealed a growth in publications, especially from 2020 onwards. Most studies use real data and apply artificial neural networks, support vector machines, and random forests. Research gaps were also identified, such as the need to develop representative models of building stocks. It is concluded that benchmarking can play a key role in building energy efficiency, and expanding databases can make the method more accurate and applicable.
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