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Pregled bibliografske jedinice broj: 908880

Modelling the impact of installation of heat cost allocators in DH systems using algorithms of Machine Learning


Maljković, Danica; Balen, Igor; Dalbelo Bašić, Bojana
Modelling the impact of installation of heat cost allocators in DH systems using algorithms of Machine Learning // http://www.dubrovnik2017.sdewes.org/
Dubrovnik, Hrvatska, 2017. str. 1-2 (predavanje, podatak o recenziji nije dostupan, sažetak, znanstveni)


CROSBI ID: 908880 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Modelling the impact of installation of heat cost allocators in DH systems using algorithms of Machine Learning

Autori
Maljković, Danica ; Balen, Igor ; Dalbelo Bašić, Bojana

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Http://www.dubrovnik2017.sdewes.org/ / - , 2017, 1-2

Skup
12th Conference on Sustainable Development of Energy, Water and Environmental Systems

Mjesto i datum
Dubrovnik, Hrvatska, 04.10.2017. - 08.10.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Podatak o recenziji nije dostupan

Ključne riječi
district heating, energy efficiency, heat cost allocateors, modelling

Sažetak
Experimental research is conducted on a set of actual energy consumption data obtained from entities in the sector over a several-year period. All factors influencing consumption are defined in terms of physical and non-physical factors. Physical factors include insulation quality, number of floors, number of rooms, the existence of individual metering and types of measuring devices, the degree of heating, etc. Non-physical factors include the number of tenants, the age of tenants, the method of calculation for the energy consumed and the energy price, the level of desired thermal comfort, etc. A model is under development including these steps: pre-processing, sampling, prediction model based on the learning set, and testing the model on the test set. In this paper, descriptive multivariate statistical analysis and explorative analysis is used to perform the prepare the data for the analysis.

Izvorni jezik
Engleski



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb


Citiraj ovu publikaciju:

Maljković, Danica; Balen, Igor; Dalbelo Bašić, Bojana
Modelling the impact of installation of heat cost allocators in DH systems using algorithms of Machine Learning // http://www.dubrovnik2017.sdewes.org/
Dubrovnik, Hrvatska, 2017. str. 1-2 (predavanje, podatak o recenziji nije dostupan, sažetak, znanstveni)
Maljković, D., Balen, I. & Dalbelo Bašić, B. (2017) Modelling the impact of installation of heat cost allocators in DH systems using algorithms of Machine Learning. U: http://www.dubrovnik2017.sdewes.org/.
@article{article, author = {Maljkovi\'{c}, Danica and Balen, Igor and Dalbelo Ba\v{s}i\'{c}, Bojana}, year = {2017}, pages = {1-2}, keywords = {district heating, energy efficiency, heat cost allocateors, modelling}, title = {Modelling the impact of installation of heat cost allocators in DH systems using algorithms of Machine Learning}, keyword = {district heating, energy efficiency, heat cost allocateors, modelling}, publisherplace = {Dubrovnik, Hrvatska} }
@article{article, author = {Maljkovi\'{c}, Danica and Balen, Igor and Dalbelo Ba\v{s}i\'{c}, Bojana}, year = {2017}, pages = {1-2}, keywords = {district heating, energy efficiency, heat cost allocateors, modelling}, title = {Modelling the impact of installation of heat cost allocators in DH systems using algorithms of Machine Learning}, keyword = {district heating, energy efficiency, heat cost allocateors, modelling}, publisherplace = {Dubrovnik, Hrvatska} }




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