engleski

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

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.

district heating, energy efficiency, heat cost allocateors, modelling

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