Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313252083> ?p ?o ?g. }
- W4313252083 endingPage "102951" @default.
- W4313252083 startingPage "102951" @default.
- W4313252083 abstract "One of the key reasons for the performance discrepancy between a building's intended usage and the actual operation is Heat Loss, which describes a building's envelope efficiency during in-use circumstances. In this setting, the ANN models’ use for energy analysis of green buildings has become more established. This research aims to anticipate the heat loss of green buildings utilizing two artificial neural network-based methodologies (ANN). In particular, TLBO and BBO are used and contrasted. Additionally, RMSE, MAE, and R2 are used to calculate an absolute error for predicting heat loss to gauge the accuracy of the findings. The suggested TLBO-MLP standard is a reliable method with a positive outcome (RMSE = 0.01012 and 0.05216, and R2 = 0.99536 and 0.9651). Also, according to the training error ranges of [−0.0006078, 0.01133] and [−0.00040708, 0.010181] and testing error ranges of [0.0004724, 0.068666] and [0.0021984, 0.057688] for BBO-MLP and TLBO-MLP, respectively, shows that the TLBO-MLP reaches the lower range of error and can predict the heat loss with higher accuracy and it could properly forecast the heat loss of building technologies. Even so, the BBO-MLP standard provides this research with satisfactory performance (R2 = 0.9943 and 0.95175, and RMSE = 0.01122 and 0.06112). To increase the precision of calculating the heat loss of buildings, specifically integrating them with optimization algorithms, further study is required." @default.
- W4313252083 created "2023-01-06" @default.
- W4313252083 creator A5015199101 @default.
- W4313252083 creator A5017257788 @default.
- W4313252083 creator A5020804907 @default.
- W4313252083 creator A5021918612 @default.
- W4313252083 creator A5080655046 @default.
- W4313252083 creator A5090498102 @default.
- W4313252083 date "2023-02-01" @default.
- W4313252083 modified "2023-10-18" @default.
- W4313252083 title "Green building’s heat loss reduction analysis through two novel hybrid approaches" @default.
- W4313252083 cites W1814738013 @default.
- W4313252083 cites W1965436778 @default.
- W4313252083 cites W1971554611 @default.
- W4313252083 cites W1999284878 @default.
- W4313252083 cites W2001422417 @default.
- W4313252083 cites W2081690420 @default.
- W4313252083 cites W2103540160 @default.
- W4313252083 cites W2114440072 @default.
- W4313252083 cites W2168081761 @default.
- W4313252083 cites W2180748755 @default.
- W4313252083 cites W2496781458 @default.
- W4313252083 cites W2569008010 @default.
- W4313252083 cites W2597219005 @default.
- W4313252083 cites W2770603703 @default.
- W4313252083 cites W2774519395 @default.
- W4313252083 cites W2776567776 @default.
- W4313252083 cites W2783933845 @default.
- W4313252083 cites W2824400775 @default.
- W4313252083 cites W2892132801 @default.
- W4313252083 cites W2898832751 @default.
- W4313252083 cites W2901312569 @default.
- W4313252083 cites W2901489802 @default.
- W4313252083 cites W2910382316 @default.
- W4313252083 cites W2918135595 @default.
- W4313252083 cites W2926155474 @default.
- W4313252083 cites W2926407963 @default.
- W4313252083 cites W2935477365 @default.
- W4313252083 cites W2944188217 @default.
- W4313252083 cites W2963595412 @default.
- W4313252083 cites W2971706201 @default.
- W4313252083 cites W2972984906 @default.
- W4313252083 cites W2991260997 @default.
- W4313252083 cites W2993177448 @default.
- W4313252083 cites W2995872447 @default.
- W4313252083 cites W2999164380 @default.
- W4313252083 cites W3002622729 @default.
- W4313252083 cites W3004454690 @default.
- W4313252083 cites W3006268427 @default.
- W4313252083 cites W3008239882 @default.
- W4313252083 cites W3008571545 @default.
- W4313252083 cites W3010223410 @default.
- W4313252083 cites W3014640470 @default.
- W4313252083 cites W3039101609 @default.
- W4313252083 cites W3091904918 @default.
- W4313252083 cites W3096137942 @default.
- W4313252083 cites W3097542115 @default.
- W4313252083 cites W3112043391 @default.
- W4313252083 cites W3158723639 @default.
- W4313252083 cites W3162406330 @default.
- W4313252083 cites W3168793044 @default.
- W4313252083 cites W3179014163 @default.
- W4313252083 cites W3214347576 @default.
- W4313252083 cites W3216591070 @default.
- W4313252083 cites W4220969554 @default.
- W4313252083 cites W4224231356 @default.
- W4313252083 cites W4229333028 @default.
- W4313252083 cites W4244190327 @default.
- W4313252083 cites W4280641021 @default.
- W4313252083 cites W4281746414 @default.
- W4313252083 cites W4297026089 @default.
- W4313252083 cites W4297533947 @default.
- W4313252083 cites W4302774011 @default.
- W4313252083 cites W4307725072 @default.
- W4313252083 doi "https://doi.org/10.1016/j.seta.2022.102951" @default.
- W4313252083 hasPublicationYear "2023" @default.
- W4313252083 type Work @default.
- W4313252083 citedByCount "1" @default.
- W4313252083 countsByYear W43132520832023 @default.
- W4313252083 crossrefType "journal-article" @default.
- W4313252083 hasAuthorship W4313252083A5015199101 @default.
- W4313252083 hasAuthorship W4313252083A5017257788 @default.
- W4313252083 hasAuthorship W4313252083A5020804907 @default.
- W4313252083 hasAuthorship W4313252083A5021918612 @default.
- W4313252083 hasAuthorship W4313252083A5080655046 @default.
- W4313252083 hasAuthorship W4313252083A5090498102 @default.
- W4313252083 hasBestOaLocation W43132520831 @default.
- W4313252083 hasConcept C105795698 @default.
- W4313252083 hasConcept C111335779 @default.
- W4313252083 hasConcept C119857082 @default.
- W4313252083 hasConcept C127413603 @default.
- W4313252083 hasConcept C139945424 @default.
- W4313252083 hasConcept C146978453 @default.
- W4313252083 hasConcept C204323151 @default.
- W4313252083 hasConcept C2524010 @default.
- W4313252083 hasConcept C33923547 @default.
- W4313252083 hasConcept C41008148 @default.
- W4313252083 hasConcept C50644808 @default.