An indoor thermal comfort model for group thermal comfort prediction based on K-means++ algorithm

•K-means++ algorithm was first used to build a thermal comfort model without occupants’ feedback.•Even with small dataset, the proposed model achieved high accurancy.•Mahalanobis distance algorithm was used for outlier test, reducing errors and avoiding excessive reduction of data set.•The model cou...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Energy and buildings Ročník 327; s. 115000
Hlavní autori:	Liu, Ying, Li, Xiangru, Sun, Cheng, Dong, Qi, Yin, Qing, Yan, Bin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 15.01.2025
Predmet:	Indoor thermal comfort K-means++ clustering algorithm Machine learning Prediction model ANN K-means++ clustering algorithm CLO Prediction model KNN WGBT HR DBI SVM DT SC Indoor thermal comfort CC SSE TOA IQR EEM TA TSK TC NB RF RH RI Machine learning VEL TP MET TR TS
ISSN:	0378-7788
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!