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

Küçüktopcu E., Cemek B.

Environmental Progress and Sustainable Energy, vol.40, no.1, 2021 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 40 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1002/ep.13478
  • Journal Name: Environmental Progress and Sustainable Energy
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, Chemical Abstracts Core, Compendex, Computer & Applied Sciences, Environment Index, Greenfile, INSPEC, Pollution Abstracts
  • Keywords: artificial neural networks, energy, insulation, model, poultry
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

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.