The use of artificial neural networks to estimate optimum insulation thickness, energy savings, and carbon dioxide emissions

This study examined artificial neural networks' (ANNs) applicability in modeling optimum insulation thickness (OIT), annual total net savings (ATS), and reduction of carbon dioxide emissions (RCO2) that result from insulating buildings. Data from insulation markets, economic parameters, fuel pr...

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Bibliographic Details
Published in:Environmental progress 2021-01, Vol.40 (1), p.n/a
Main Authors: Küçüktopcu, Erdem, Cemek, Bilal
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Bayesian analysis
Carbon dioxide
Carbon dioxide emissions
Emissions
energy
Energy conservation
Insulation
Learning algorithms
Machine learning
Mathematical models
Neural networks
poultry
Pricing
Regularization
Root-mean-square errors
Standard error
Statistical analysis
Thermal insulation
Thickness
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Summary:This study examined artificial neural networks' (ANNs) applicability in modeling optimum insulation thickness (OIT), annual total net savings (ATS), and reduction of carbon dioxide emissions (RCO2) that result from insulating buildings. Data from insulation markets, economic parameters, fuel prices, and heating degree days (HDDs) were introduced into the model as input variables. To complete the most thorough analysis, three learning algorithms, Levenberg Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) were employed. Five statistical indexes were utilized to evaluate models' performances: determination coefficient (R2), root mean square error (RMSE), standard error of prediction (SEP), RMSE observations' standard deviation ratio (RSR), and average absolute percent relative error (AAPRE). Moreover, visualization techniques were used to assess the similarity between the OIT, ATS, and RCO2 values calculated and predicted. The results obtained clearly show that the LM model outperformed the BR and SCG models in all estimations. Thereafter, the developed ANNs model was validated for different cities. Overall, this model will provide an effective and straightforward guide for people working in the field to improve thermal insulation design, analysis, and implementation worldwide.
ISSN:1944-7442
1944-7450
DOI:10.1002/ep.13478