Науковий вісник НГУ

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

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №2 2021

Last Updated on 29 April 2021

Published on 30 November -0001

Hits: 3819

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

References.

1. Perekrest, A., Shendryk, V., Pijarski, P., Parfenenko, Y., & Shendryk, S. (2017). Complex information and technical solutions for energy management of municipal energetics. Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017, 10445, 1044567. https://doi.org/10.1117/12.2280962.

2. Perekrest, A., Konokh, I., & Kushch-Zhyrko, M. (2019). Administrative Buildings Heating Automatic Control Based on Maximum Efficiency Criterion. 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES), 202-205. https://doi.org/10.1109/mees.2019.8896517.

3. Ramos, S., Duarte, J., Soares, J., Vale, Z., & Duarte, F. (2012). Typical load profiles in the smart grid context: A clustering methods comparison. 2012 IEEE Power and Energy Society General Meeting. https://doi.org/10.1109/pesgm.2012.6345565.

4. Chebotarova, Y., Perekrest, A., & Ogar, V. (2019). Comparative Analysis of Efficiency Energy Saving Solutions Implemented in the Buildings. 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES). Kremenchuk, Ukraine, 434-437. https://doi.org/10.1109/MEES.2019.8896691.

5. Ma, Z., Xie, J., Li, H., Sun, Q., Si, Z., Zhang, J., & Guo, J. (2017). The Role of Data Analysis in the Development of Intelligent Energy Networks. IEEE Network, 31(5), 88-95. https://doi.org/10.1109/mnet.2017.1600319.

6. Power institute Hrvoje Poar (2020). Research on the current state of energy management in Ukrainian communities. Retrieved from http://misto-em.org.ua/wp-content/uploads/2020/07/Zvit-pro-doslidzhennya-MEM-v-gromadah_ukr.pdf.

7. Du, Y., Wang, Ch., Li, H., Song, J., & Li, B. (2019). Clustering Heat Users Based on Consumption Data. Energy Procedia, 158, 3196-3201. https://doi.org/10.1016/j.egypro.2019.01.1010.

8. Wang, C., Du, Y., Li, H., Wallin, F., & Min, G. (2019). New methods for clustering district heating users based on consumption patterns. Applied Energy, 251, 113373. https://doi.org/10.1016/j.apenergy.2019.113373.

9. Do Carmo, C., & Christensen, T. (2016). Cluster analysis of residential heat load profiles and the role of technical and household characteristics. Energy And Buildings, 125, 171-180. https://doi.org/10.1016/j.enbuild.2016.04.079.

10. Gianniou, P., Liu, X., Heller, A., Nielsen, P., & Rode, C. (2018). Clustering-based analysis for residential district heating data. Energy Conversion and Management, 165, 840-850. https://doi.org/10.1016/j.enconman.2018.03.015.

11. Tureczek, A., Nielsen, P., Madsen, H., & Brun, A. (2019). Clustering district heat exchange stations using smart meter consumption data. Energy and Buildings, 182, 144-158. https://doi.org/10.1016/j.enbuild.2018.10.009.

12. Aiad, M., & Lee, P. (2018). Energy disaggregation of overlapping home appliances consumptions using a cluster splitting approach. Sustainable Cities and Society, 43, 487-494. https://doi.org/10.1016/j.scs.2018.08.020.

13. Diao, L., Sun, Y., Chen, Z., & Chen, J. (2017). Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation. Energy and Buildings, 147, 47-66. https://doi.org/10.1016/j.enbuild.2017.04.072.

14. Azaza, M., & Wallin, F. (2017). Smart meter data clustering using consumption indicators: responsibility factor and consumption variability. Energy Procedia, 142, 2236-2242. https://doi.org/10.1016/j.egypro.2017.12.624.

15. De la Puente-Gil, A., Gonzlez-Martnez, A., Borge-Diez, D., Martnez-Cabero, M.-., & de Simn-Martn, M. (2019). True power consumption labeling and mapping of the health system of the Castilla y Len region in Spain by clustering techniques. Energy Procedia, 157, 1164-1181. https://doi.org/10.1016/j.egypro.2018.11.283.

16. Prez-Ortega, J., Nely Almanza-Ortega, N., Vega-Villalobos, A., Pazos-Rangel, R., Zavala-Daz, C., & Martnez-Rebollar, A. (2019). The K-Means Algorithm Evolution.Introduction to Data Science and Machine Learning. https://doi.org/10.5772/intechopen.85447.

17. Koren, O., Hallin, C., Perel, N., & Bendet, D. (2019). Enhancement of the K-Means Algorithm for Mixed Data in Big Data Platforms. Proceedings of the 2018 Intelligent Systems Conference (IntelliSys), London, UK, 1, 1025-1040. https://doi.org/10.1007/978-3-030-01054-6_71.

18. Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2019). NbClust Package. An examination of indices for determining the number of clusters.

19. Perekrest, A., Chornyi, O., Mur, O., Kuznetsov, V., Kuznetsova, Y., & Nikolenko, A. (2018). Preparation and preliminary analysis of data on energy consumption by municipal buildings. Eastern-European Journal Of Enterprise Technologies, 6(8(96)), 32-42. https://doi.org/10.15587/1729-4061.2018.147485.

20. Covenant of Mayors. Retrieved from http://com-east.eu/uk/pro-nas/ugoda-meriv/.

• < Prev
• Next >