20.500.12556/DKUM-95549 Micro-location temperature prediction leveraging deep learning approaches Nowadays, technological progress has promoted the integration of artificial intelligence into modern human lives rapidly. On the other hand, extreme weather events in recent years have started to influence human well-being. As a result, these events have been addressed by artificial intelligence methods more and more frequently. In line with this, the paper focuses on searching for predicting the air temperature in a particular Slovenian micro-location by using a weather prediction model Maximus based on a longshort term memory neural network learned by the long-term, lower-resolution dataset CERRA. During this huge experimental study, the Maximus prediction model was tested with the ICON-D2 general-purpose weather prediction model and validated with real data from the mobile weather station positioned at a specific micro-location. The weather station employs Internet of Things sensors for measuring temperature, humidity, wind speed and direction, and rain, while it is powered by solar cells. The results of comparing the Maximus proposed prediction model for predicting the air temperature in micro-locations with the general-purpose weather prediction model ICON-D2 has encouraged the authors to continue searching for an air temperature prediction model at the micro-location in the future. long short-term memory neural networks air temperature micro-location prediction weather Internet of Things nevronske mreže temperatura zraka mikro lokacije vreme internet stvari true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/applsci © 2025 by the authors Članek v reviji 2025-09-25 08:45:04 2025-09-25 08:45:04 2025-09-26 03:13:50 0000-00-00 00:00:00 2025 0 0 26 str. iss. 12, [article no.] 6793 Vol. 15 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-05-13 2025-06-13 2025-06-17 004.8 249547011 10.3390/app15126793 2076-3417 applsci-15-06793_(1).pdf applsci-15-06793_(1).pdf 1 E8BF92492695F3A33C7496BD1896D733 0e4f3e342b2bddee0047e48eecf93ffd2b9f3411eea8b1daa55ebb7ecfa4cf61 6185b71a-99db-11f0-a1be-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=199168 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0