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Segmentation of heat energy consumers based on data on daily power consumption

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Category: Content №2 2021

Last Updated on 29 April 2021

Published on 30 November -0001

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Authors:

M.Zagirnyak, orcid.org/0000-0003-4700-0967, Kremenchuk Mykhailo Ostrohradskyi National University, Kremenchuk, Ukraine; email: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.Perekrest, orcid.org/0000-0002-7728-9020, Kremenchuk Mykhailo Ostrohradskyi National University, Kremenchuk, Ukraine; email: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.Ogar, orcid.org/0000-0003-3719-5369, Kremenchuk Mykhailo Ostrohradskyi National University, Kremenchuk, Ukraine; email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ye.Chebotarova, orcid.org/0000-0003-4725-7914, Kremenchuk Mykhailo Ostrohradskyi National University, Kremenchuk, Ukraine; email: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.Mur, freelancer, Kremenchuk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2021, (2): 089 - 096

https://doi.org/10.33271/nvngu/2021-2/089

Abstract:

Purpose. Improving the quality of the analysis of energy consumption modes of buildings of educational institutions by determining the typical patterns of their consumption by the k-means method based on statistics of thermal energy consumed, their area, outdoor and indoor air temperature.

Methodology. Methods of statistical and intellectual data analysis, optimization methods, k-means method.

Findings. It is shown that most of the researched consumers of thermal energy have similar patterns of its consumption and can be divided into separate segments according to the peculiarities of functioning. The used data set is diverse as it includes three groups of data (conditionally formed by the type of educational institutions). The buildings in groups have different areas, internal temperature to be maintained, operation modes of heating systems and so on. For each of the groups, the number of clusters corresponding to low, medium and high values of heat consumption is determined. It was found that the amount of the consumed heat and consumer behavior depends on the day of the week. The obtained results can be difficult to generalize for all existing types of buildings, but it is expected that communal buildings may have similar consumption patterns, as operating modes and norms of internal microclimate remain the same for different temperature zones and territories.

Originality. The method for dividing the consumers of thermal energy into segments on the basis of the data on daily energy consumption applying the k-means clustering algorithm, implemented in the programming language R, is further developed. For the first time, it has been proposed to consider not one, but three groups of objects of the educational institutions. It is proposed to determine the modes of energy consumption by buildings analytically by taking into account such parameters as the area of buildings, the amount of heat consumed by them, external and internal air temperature.

Practical value. The results of the research are useful for heat supply companies, municipalities and can be used to develop programs and policies for energy efficiency. The given information and analytical support create a basis for the development of software solutions with the function of integration into local energy monitoring systems of a separate building, energy management system of the district and/or city. The presented results provide an opportunity to predict the cost of energy resources used for heating buildings, which increases the efficiency of engineering systems of buildings.

Keywords: heat consumption, k-means; segmentation, clustering, data analysis

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