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

Küçüktopcu E., Cemek B.

ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY, cilt.40, sa.1, 2021 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 1
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1002/ep.13478
  • Dergi Adı: ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
  • Derginin Tarandığı İndeksler: 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
  • Anahtar Kelimeler: artificial neural networks, energy, insulation, model, poultry
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

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 (R-2), 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 RCO(2)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.