Multi-gene genetic programming for predicting the heat gain of flat naturally ventilated roof using data from outdoor environmental monitoring
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{MAYTZUC:2019:Measurement,
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author = "O. {May Tzuc} and I. Hernandez-Perez and
E. V. Macias-Melo and A. Bassam and J. Xaman and B. Cruz",
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title = "Multi-gene genetic programming for predicting the heat
gain of flat naturally ventilated roof using data from
outdoor environmental monitoring",
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journal = "Measurement",
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volume = "138",
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pages = "106--117",
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year = "2019",
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keywords = "genetic algorithms, genetic programming, Heat gains,
Building thermal measurement, Thermal comfort, Machine
learning, Evolutionary programming, Sensitivity
analysis",
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ISSN = "0263-2241",
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DOI = "doi:10.1016/j.measurement.2019.02.032",
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URL = "http://www.sciencedirect.com/science/article/pii/S0263224119301502",
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abstract = "In this work, a multi-gene genetic programming (MGGP)
approach was implemented to predict the heat gain per
square meter for flat naturally ventilated roof using
experimental data set. Experiments were conducted using
a test cell with an adjustable ventilated roof,
designed and instrumented to measure the incoming heat
flux under outdoor environmental conditions. An MGGP
predictive model was trained and tested considering as
input data: ambient air temperature, solar irradiation,
wind speed, relative humidity, and different ventilated
flat roof channel widths. The developed model was
statistically compared with others multivariate
analysis methods, achieving good statistical
performance, high correlation fitness, and the best
generalized performance capacity (RMSEa =a 3.74, R2a =a
94.52percent for training data and RMSEa =a 3.72, R2a
=a 94.30percent for testing data). In addition, a
sensitivity analysis was conducted to identify the
relative importance of the input parameters in the
predictive model. According to the results, the
proposed methodology based on evolutionary programming
is useful to model the complex nonlinear relationship
between the ventilated roof heat gains and outdoor
environment. Finally, the methodology based on MGGP can
be applied to identify the adequate ventilated channel
widths that ensure thermal comfort and energy saving",
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keywords = "genetic algorithms, genetic programming, Heat gains,
Building thermal measurement, Thermal comfort, Machine
learning, Evolutionary programming, Sensitivity
analysis",
- }
Genetic Programming entries for
Oscar de Jesus May Tzuc
Ivan Hernandez-Perez
Edgar Vicente Macias Melo
Ali Bassam
J Xaman
Braulio Cruz Jimenez
Citations