Ozbey, Mehmet FurkanLotfi, BahramTurhan, Cihan2025-03-052025-03-0520251751-25491756-220110.1080/17512549.2025.24576502-s2.0-85216452348https://doi.org/10.1080/17512549.2025.2457650https://hdl.handle.net/20.500.14411/10473Thermal comfort describes an occupant's state of mind in a thermal environment, influenced by six parameters: air velocity, relative humidity, air temperature, mean radiant temperature (MRT), clothing value, and metabolic rate. MRT is the most problematic parameter since the obtaining process is difficult and time-consuming. MRT can be acquired by several methods such as calculations, measurements, assumptions, and software programmes. However, the methods have complexities and uncertainties. Comprehensive models are needed to obtain MRT. To this aim, this study presents an alternative method using one of the artificial intelligence methods, Artificial Neural Network (ANN), to predict MRT for indoor environments to abstain from the difficulties and complexities. A case building is selected in a university office building in Ankara, T & uuml;rkiye. The proposed model is developed and coded in a Python programming environment to predict the MRT using ANN. The results indicate that the ANN model, using only four inputs, predicts MRT with an R-2 value of 0.94 compared to the globe thermometer measurement method. The model's advantages over methods include simplicity, time efficiency and learning from the limited datasets such as difficulty in calculating terms like MRT.eninfo:eu-repo/semantics/closedAccessThermal ComfortArtificial Neural NetworkMean Radiant TemperatureIndoor EnvironmentOffice BuildingsArticleN/AQ2WOS:001409585500001