Developing an Alternative Calculation Method for the Smart Readiness Indicator Based on Genetic Programming and Linear Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8355
- @Article{Beras:2025:Buildings,
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author = "Mitja Beras and Miran Brezocnik and Uros Zuperl and
Miha Kovacic",
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title = "Developing an Alternative Calculation Method for the
Smart Readiness Indicator Based on Genetic Programming
and Linear Regression",
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journal = "Buildings",
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year = "2025",
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volume = "15",
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number = "10",
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pages = "Article no: 1675",
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month = "15 " # may,
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keywords = "genetic algorithms, genetic programming, SRI,
modeling, linear regression, energy efficient
buildings, smart buildings, optimisation",
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ISSN = "2075-5309",
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URL = "
https://www.mdpi.com/2075-5309/15/10/1675",
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DOI = "
doi:10.3390/buildings15101675",
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size = "36 pages",
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abstract = "The European Union is planning to introduce a new tool
for evaluating smart solutions in buildingsāthe Smart
Readiness Indicator (SRI). As 54 energy efficiency
categories must be evaluated, the triage process can be
long and time-intensive. Altogether, 228 data points
(or inputs) about the smartness of the buildings are
required to complete the evaluation. The present paper
proposes an alternative calculation method based on
genetic programming (GP) for the calculation of Domains
and linear regression (LR) for the calculation of
Impact Factors and the total SRI score of the building.
This novel calculation requires 20percent (Domain
ventilation and dynamic building envelope) to 75percent
(Domain cooling) fewer inputs than the original
methodology. The present study evaluated 223 case study
buildings, and 7 genetic programming models and 8
linear regression models were generated based on the
results. The generated results are precise; the
relative deviation from the experimental data for
Domain scores (modeled with GP) ranged from 0.9percent
to 2.9percent. The R2 for the LR models was 0.75 for
most models (with two exceptions, with one with a value
of 0.57 and the other with a value of 0.98). The
developed method is scalable and could be used for
preliminary and portfolio-level screening at
early-stage assessments.",
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notes = "Also known as \cite{buildings15101675}
Faculty of Mechanical Engineering, University of
Maribor, 2000 Maribor, Slovenia",
- }
Genetic Programming entries for
Mitja Beras
Miran Brezocnik
Uros Zuperl
Miha Kovacic
Citations