Naslov
Modelling the impact of installation of heat cost allocators in DH systems using decision tree model

Autori
Maljković, Danica ; Balić, Dražen

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

Izvornik
Book of Abstracts, 2nd International Conference on Smart Energy Systems and 4th Generation District Heating / - , 2016

Skup
2nd International Conference on Smart Energy Systems and 4th Generation District Heating

Mjesto i datum
Aalborg, Danska, 27.09.2016. - 28.09.2016

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
district heating; heat cost allocator; energy efficiency; machine learning; decision tree model

Sažetak
Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia being one of these states. The Directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. Heat consumption is dependent on a number of factors, such as heating degree days, building envelope characteristics, occupancy, existence of individual metering as a basis for change in user’s behaviour, etc. It is a complex system to model the exact influence of the change of one of the heat consumption factors on overall consumption. In previous research there have been a number of studies on building energy consumption modelling and the usual methods used are traditional multiple regression models, simulation methods and methods of artificial neural networks. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method with machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper a decision tree method will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

POVEZANOST RADA