Preparation and preliminary analysis of data on energy consumption by municipal buildings

Authors

DOI:

https://doi.org/10.15587/1729-4061.2018.147485

Keywords:

heat supple to buildings, energy consumption data analysis, data preparation, monitoring of energy consumption by buildings

Abstract

Systematization of data on energy consumption by buildings of different purposes makes it possible to investigate processes from the standpoint of efficient use of energy resources in order to ensure comfortable conditions. This necessitates improvement of existing approaches, or search for the new ones, in order to analyze data on energy consumption by different buildings.

Based on a study into the process of preparing data on energy consumption by buildings, we have proposed a procedure of initial analysis. It takes into account the purpose of a building, as well as techniques for data acquisition, information on the indicators of absolute and relative electricity­ and heat consumption, indicators of indoor and outdoor air temperatures. Using energy consumption by buildings of the educational institution as an example, we have verified the devised procedure for the preliminary data analysis.

Our study has made it possible to establish the correlation of energy consumption indicators and the indoor and outdoor air temperatures in the transition from general data to the data on a heating period. An analysis of spread diagrams has revealed the trends towards lower energy consumption, as well as the excessive consumption of energy resources by the examined objects.

Based on the developed software, we compared indicators of heat consumption by individual apartments and a maximum heating need in accordance with normative documents.

The results obtained could form a basis for developing applied information solutions for municipal energy management

Author Biographies

Andrii Perekrest, Kremenchuk Mykhailo Ostrohradskyi National University Pershotravneva str., 20, Kremenchuk, Ukraine, 39600

PhD, Associate Professor

Department of automation and computer-integrated technologies

Oleksii Chornyi, Institute of Electromechanics, Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrohradskyi National University Pershotravneva str., 20, Kremenchuk, Ukraine, 39600

Doctor of Technical Sciences, Professor, Director of Institute

Oleksandra Mur, Kremenchuk Mykhailo Ostrohradskyi National University Pershotravneva str., 20, Kremenchuk, Ukraine, 39600

Department of automation and computer-integrated technologies

Vitaliy Kuznetsov, National Metallurgical Academy of Ukraine Gagarina ave., 4, Dnipro, Ukraine, 49600

PhD, Associate Professor

Department of the electrical engineering and electromechanic

Yevheniia Kuznetsova, Institute of Integrated Education National Metallurgical Academy of Ukraine Gagarina ave., 4, Dnipro, Ukraine, 49600

Senior Lecturer

Department of humanitarian, fundamental and general engineering disciplines

Anatoliy Nikolenko, National Metallurgical Academy of Ukraine Gagarina ave., 4, Dnipro, Ukraine, 49600

PhD, Associate Professor, Head of Department

Department of the electrical engineering and electromechanic

References

  1. Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J. et. al. (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews, 82, 1027–1047. doi: https://doi.org/10.1016/j.rser.2017.09.108
  2. Perekrest, A. L., Romanenko, S. S. (2015). Scientific and applied aspects saving energy and resources in the municipal energy sector. Elektromekhanichni i enerhozberihaiuchi systemy, 2, 162–170.
  3. 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. doi: https://doi.org/10.1117/12.2280962
  4. Balco, P., Drahošová, M., Kubičko, P. (2018). Data analysis in process of energetics resource optimization. Procedia Computer Science, 130, 597–602. doi: https://doi.org/10.1016/j.procs.2018.04.109
  5. Zakovorotnyi, A., Seerig, A. (2017). Building energy data analysis by clustering measured daily profiles. Energy Procedia, 122, 583–588. doi: https://doi.org/10.1016/j.egypro.2017.07.353
  6. Arregi, B., Garay, R. (2017). Regression analysis of the energy consumption of tertiary buildings. Energy Procedia, 122, 9–14. doi: https://doi.org/10.1016/j.egypro.2017.07.290
  7. Ruiz, L. G. B., Rueda, R., Cuéllar, M. P., Pegalajar, M. C. (2018). Energy consumption forecasting based on Elman neural networks with evolutive optimization. Expert Systems with Applications, 92, 380–389. doi: https://doi.org/10.1016/j.eswa.2017.09.059
  8. Ahmad, M. W., Mourshed, M., Rezgui, Y. (2017). Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147, 77–89. doi: https://doi.org/10.1016/j.enbuild.2017.04.038
  9. Jeffrey Kuo, C.-F., Lin, C.-H., Lee, M.-H. (2018). Analyze the energy consumption characteristics and affecting factors of Taiwan's convenience stores-using the big data mining approach. Energy and Buildings, 168, 120–136. doi: https://doi.org/10.1016/j.enbuild.2018.03.021
  10. Parfenenko, Yu. V., Shendryk, V. V., Galichenko, O. S. (2015). Prediction the heat consumption of social and public sector buildings using neural networks. Radioelektronika, informatyka, upravlinnia, 2, 41–46. Available at: http://nbuv.gov.ua/UJRN/riu_2015_2_7
  11. Slabchenko, O., Sydorenko, V., Siebert, X. (2016). Development of models for imputation of data from social networks on the basis of an extended matrix of attributes. Eastern-European Journal of Enterprise Technologies, 4 (2 (82)), 24–34. doi: https://doi.org/10.15587/1729-4061.2016.74871
  12. Geng, Y., Ji, W., Lin, B., Hong, J., Zhu, Y. (2018). Building energy performance diagnosis using energy bills and weather data. Energy and Buildings, 172, 181–191. doi: https://doi.org/10.1016/j.enbuild.2018.04.047
  13. Babaei, T., Abdi, H., Lim, C. P., Nahavandi, S. (2015). A study and a directory of energy consumption data sets of buildings. Energy and Buildings, 94, 91–99. doi: https://doi.org/10.1016/j.enbuild.2015.02.043
  14. Perekrest, A. L., Zagirnyak, M. V. (2014). Opyt vnedreniya i ispol'zovaniya avtomatizirovannoy sistemy monitoringa temperaturnyh rezhimov i udalennogo upravleniya teplopotrebleniem Kremenchugskogo nacional'nogo universiteta. Elektrotekhnicheskie i komp'yuternye sistemy, 15 (91), 423–426.

Downloads

Published

2018-11-16

How to Cite

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

Issue

Section

Energy-saving technologies and equipment