Energy consumption predictions by genetic programming methods for PCM integrated building in the tropical savanna climate zone
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- @Article{NAZIR:2023:jobe,
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author = "Kashif Nazir and Shazim Ali Memon and
Assemgul Saurbayeva and Abrar Ahmad",
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title = "Energy consumption predictions by genetic programming
methods for {PCM} integrated building in the tropical
savanna climate zone",
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journal = "Journal of Building Engineering",
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volume = "68",
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pages = "106115",
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year = "2023",
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ISSN = "2352-7102",
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DOI = "doi:10.1016/j.jobe.2023.106115",
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URL = "https://www.sciencedirect.com/science/article/pii/S2352710223002942",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, Phase change materials,
Early-stage design parameters, Energy consumption,
Multi-expression programming, Gene-expression
programming",
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abstract = "The development of energy-efficient buildings by
considering early-stage design parameters can help
reduce buildings' energy consumption. Machine learning
tools are getting popular for forecasting the energy
demand of buildings, which play a vital role in
improving building energy efficiency. In this research,
multi-expression and genetic expression programming
were used to anticipate the energy consumption of
PCM-integrated buildings by taking early-stage design
parameters into consideration. The prediction models
were developed using the data generated by energy
simulations for the PCM-integrated building in eight
cities within a tropical savanna climate. The statical
parameters were used to evaluate and externally
validate the proposed prediction model. The statistical
evaluation reveals that the genetic expression
programming-based predictive model gave more accurate
energy consumption predictions for PCM-integrated
buildings than multi-expression programming. The
performance indices of the statistically analyzed gene
expression programming-based prediction model (GEP7)
showed excellent values: correlation coefficient (R) =
0.961, performance index (rho) = 0.169, and
Nash-Sutcliffe efficiency (NSE) = 0.108. Thereafter,
the sensitivity and parametric analyses were performed.
It was unearthed that the roof solar absorptance,
window visible transmittance, wall solar absorptance,
and the melting temperature of PCM were the influential
early-stage design parameters for PCM-integrated
buildings. In conclusion, the gene-expression
programming-based predictive model can be used to
predict the influence of early-stage design parameters
on the energy consumption of PCM-integrated buildings",
- }
Genetic Programming entries for
Kashif Nazir
Shazim Ali Memon
Assemgul Saurbayeva
Abrar Ahmad
Citations