Predviđanje potrošnje KGH sistema primenom metoda veštačke inteligencije
2016
Autori
Sretenović, AleksandraOstala autorstva
Živković, BranislavJovanović, Radiša
Miljković, Zoran
Jovović, Aleksandar
Jovanović Popović, Milica
Doktorska teza (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
S obzirom da je sektor zgradarstva u Evropi odgovoran za 40% ukupne potrošnje energije, kao i za 36% ukupne emisije SO2, energertska efikasnost, a samim tim i analiza potrošnje energije su teme od velikog značaja...
Due to the fact that in Europe buildings account for 40% of total energy use and 36% of total CO2 emission estimation or prediction of building energy consumption is lately topic of greatest interest. This research filed involves various scientific domains. The main idea of this dissertation is to investigate application of artificial intelligence in building energy use prediction. In the statistical (data-driven) approach it is required that the input and output variables are known and measured, and the development of the “black box” model consists in determination of a mathematical description of the relationship between the independent variables and the dependent one...
Ključne reči:
višestepeni ansambli / veštačke neuronske mreže / veštačka inteligencija / predviđanje potrošnje energije zgrada / metoda potpornih vektora / hibridni modeli / energetska efikasnost / support vector machine / multistage ensemble / hybrid models / energy efficiency / energy consumption prediction / artificial neural networks / artificial intelligenceIzvor:
2016Izdavač:
- Univerzitet u Beogradu, Mašinski fakultet
URI
http://eteze.bg.ac.rs/application/showtheses?thesesId=4657https://nardus.mpn.gov.rs/handle/123456789/7718
https://fedorabg.bg.ac.rs/fedora/get/o:14828/bdef:Content/download
http://vbs.rs/scripts/cobiss?command=DISPLAY&base=70036&RID=514769059
https://machinery.mas.bg.ac.rs/handle/123456789/43
Kolekcije
Institucija/grupa
Mašinski fakultetTY - THES AU - Sretenović, Aleksandra PY - 2016 UR - http://eteze.bg.ac.rs/application/showtheses?thesesId=4657 UR - https://nardus.mpn.gov.rs/handle/123456789/7718 UR - https://fedorabg.bg.ac.rs/fedora/get/o:14828/bdef:Content/download UR - http://vbs.rs/scripts/cobiss?command=DISPLAY&base=70036&RID=514769059 UR - https://machinery.mas.bg.ac.rs/handle/123456789/43 AB - S obzirom da je sektor zgradarstva u Evropi odgovoran za 40% ukupne potrošnje energije, kao i za 36% ukupne emisije SO2, energertska efikasnost, a samim tim i analiza potrošnje energije su teme od velikog značaja... AB - Due to the fact that in Europe buildings account for 40% of total energy use and 36% of total CO2 emission estimation or prediction of building energy consumption is lately topic of greatest interest. This research filed involves various scientific domains. The main idea of this dissertation is to investigate application of artificial intelligence in building energy use prediction. In the statistical (data-driven) approach it is required that the input and output variables are known and measured, and the development of the “black box” model consists in determination of a mathematical description of the relationship between the independent variables and the dependent one... PB - Univerzitet u Beogradu, Mašinski fakultet T1 - Predviđanje potrošnje KGH sistema primenom metoda veštačke inteligencije UR - https://hdl.handle.net/21.15107/rcub_nardus_7718 ER -
@phdthesis{ author = "Sretenović, Aleksandra", year = "2016", abstract = "S obzirom da je sektor zgradarstva u Evropi odgovoran za 40% ukupne potrošnje energije, kao i za 36% ukupne emisije SO2, energertska efikasnost, a samim tim i analiza potrošnje energije su teme od velikog značaja..., Due to the fact that in Europe buildings account for 40% of total energy use and 36% of total CO2 emission estimation or prediction of building energy consumption is lately topic of greatest interest. This research filed involves various scientific domains. The main idea of this dissertation is to investigate application of artificial intelligence in building energy use prediction. In the statistical (data-driven) approach it is required that the input and output variables are known and measured, and the development of the “black box” model consists in determination of a mathematical description of the relationship between the independent variables and the dependent one...", publisher = "Univerzitet u Beogradu, Mašinski fakultet", title = "Predviđanje potrošnje KGH sistema primenom metoda veštačke inteligencije", url = "https://hdl.handle.net/21.15107/rcub_nardus_7718" }
Sretenović, A.. (2016). Predviđanje potrošnje KGH sistema primenom metoda veštačke inteligencije. Univerzitet u Beogradu, Mašinski fakultet.. https://hdl.handle.net/21.15107/rcub_nardus_7718
Sretenović A. Predviđanje potrošnje KGH sistema primenom metoda veštačke inteligencije. 2016;. https://hdl.handle.net/21.15107/rcub_nardus_7718 .
Sretenović, Aleksandra, "Predviđanje potrošnje KGH sistema primenom metoda veštačke inteligencije" (2016), https://hdl.handle.net/21.15107/rcub_nardus_7718 .