Pregled bibliografske jedinice broj: 1228116
Identification of Window Openness in Smart Buildings by Random Forest Algorithm
Identification of Window Openness in Smart Buildings by Random Forest Algorithm // Proceedings of the 5th International Conference on Smart Systems and Technologies (SST 2022)
Osijek, Hrvatska, 2022. str. 105-110 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1228116 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Identification of Window Openness in Smart Buildings
by Random Forest Algorithm
Autori
Jelić, Luka ; Kenda, Jurica ; Banjac, Anita ; Rukavina, Filip ; Lešić, Vinko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 5th International Conference on Smart Systems and Technologies (SST 2022)
/ - , 2022, 105-110
Skup
International Conference on Smart Systems and Technologies 2022 (SST 2022)
Mjesto i datum
Osijek, Hrvatska, 19.10.2022. - 21.10.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
machine learning, random forest, smart buildings, user behavior, window openness
Sažetak
Controlled energy distribution and energy efficiency in buildings are among the main concerns in modern constructions, energy management and buildings usability. Total energy performance of a building depends on several factors, which include materials and components, building environment, but also the occupants behavior. User interaction with windows and window status as open or closed is the main cause of the difference between the predicted and the actual energy consumption. Understanding these user habits helps to better predict energy disruptions and enables planning for more efficient energy distribution. This paper investigates the possibility of using a machine learning model to identify the user behavior through a set of available historical data in a living-lab smart building. The random forest algorithm used in this research proved promising for the particular use case and makes a good ground for future work with window status classification accuracy of 87.85%.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Projekti:
HRZZ-UIP-2020-02-9636 - Distribuirano upravljanje za dinamičko gospodarenje energijom u složenim sustavima naprednih gradova (DECIDE) (Lešić, Vinko, HRZZ - 2020-02) ( CroRIS)