Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings
Created by W.Langdon from
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- @Article{ALSHARIF:2022:JBE,
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author = "Rashed Alsharif and Mehrdad Arashpour and
Emadaldin Mohammadi Golafshani and M. Reza Hosseini and
Victor Chang and Jenny Zhou",
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title = "Machine learning-based analysis of occupant-centric
aspects: Critical elements in the energy consumption of
residential buildings",
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journal = "Journal of Building Engineering",
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volume = "46",
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pages = "103846",
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year = "2022",
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ISSN = "2352-7102",
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DOI = "doi:10.1016/j.jobe.2021.103846",
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URL = "https://www.sciencedirect.com/science/article/pii/S2352710221017046",
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keywords = "genetic algorithms, genetic programming, Artificial
Intelligence, Energy simulation, Metabolic rate,
Predicted mean vote (PMV), Sustainability",
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abstract = "The housing sector consumes a significant amount of
energy worldwide, which is mainly attributed to
operating energy systems for the provision of thermally
comfortable indoor environments. Although the
literature in this field has focused on investigating
critical factors in energy consumption, only a few
studies have conducted a quantitative sensitivity
analysis for thermal occupant factors (TOF) (i.e.,
metabolic rate and clothing level). Therefore, this
paper introduces a framework for testing the
criticality of TOF with a cross-comparison against
building-related factors, considering the constraint of
occupant thermal comfort. Using a building energy
simulation model, the energy consumption of a case
study is simulated, and building energy model
alternatives are generated. The scope includes TOF and
building envelope factors, with an established
orthogonal experimental design. A popular branch of
machine learning (ML) called linear genetic programming
(LGP) is used to analyse the generated data from the
experiment. Finally, a sensitivity analysis is
conducted using the developed LGP model to determine
and rank the criticality of the considered factors. The
findings reveal that occupants' metabolic rate and
clothing level have relevancy factors of -0.48 and
-0.38 respectively, which ranked them 2nd and 3rd
against building envelope factors for achieving
energy-efficient comfortable houses. This research
contributes to the literature by introducing a
framework that couples orthogonal experiment design
with ML techniques to quantify the criticality of TOF
and rank them against building-envelope factors",
- }
Genetic Programming entries for
Rashed Alsharif
Mehrdad Arashpour
Emadaldin Mohammadi Golafshani
M Reza Hosseini
Victor Chang
Jenny Zhou
Citations