Forecast of demand for the rmal energy for buildings of secondary educational institutions based on the properties of heteromorphism of their energy systems

THE PURPOSE. Improving the accuracy of forecast calculations of demand for energy resources is an urgent task, especially in the light of the Digital Energy of the Russian Federation program. Prediction is also required for he at supply systems. The complexity of the analysis is the lack of confirmation of the similarity properties of energy systems and complexes for buildings with similar functionality. On the example of buildings of secondary educational institutions located i n the territory of Moscow, the assumption of heteromorphism of thermal systems is proved. METHODS. In the work, an assumption was made that there were no significant changes in the data on the heat consumption of the energy facilities of schools, which was confirmed by the absence of changes in the average annual heat consumption and jumps in the monthly heat consumption diagrams. The amount of heat energy consumption measured and transferred to the IS is influenced by a number of additional factors: accura cy drift of heat energy metering devices; aging and overgrowing of the internal surfaces of the building's heating network equipment; physical aging and deterioration of the building envelope and deterioration of their thermal insulation performance. When compiling predicted energy consumption, this means that it is permissible to use not only statistical data about the analyzed object itself, but also about a variety of objects similar to those analyzed in structure and functionality. RESULTS. A set of input factors is proposed that makes it possible to accurately determine the predicted demand for thermal energy for buildings of secondary educational institutions. The possibility and similar accuracy of the results of forecasting the demand for thermal ene rgy is shown both through the use of multivariate regression analysis and artificial neural networks. CONCLUSION. ЭBased on the combined use of various mathematical approaches, it is proposed to use the methodology for forecasting energy demand by energy complexes and systems as a mechanism for determining the correctness of the transmitted meter readings.

comparable conditions, forecasting, demand for thermal energy, artificial neural network, error

1. Strategiya tsifrovoy transformatsii v elektroenergetike. [Elektronnyy resurs]. Available at: URL: https://www.digital-energy.ru/activity/materials/strategy-for-digital-transformation-electric-power-industry/ Accessed to: May 20, 2020.

2. Informatsionnaya sistema energosberezheniya Departamenta zhilishchno -kommunal'nogo khozyaystva goroda Moskvy [Elektronnyy resurs]. Available at: URL: https://ise.mos.ru/ (data Accessed to: May 20, 2020.

3. Guzhov SV. Issledovaniye i prognozirovaniye kachestva funktsionirovaniya energosistem zdaniy dlya zanyatiy vodnymi vidami sporta pri raznoobraznykh vneshnikh vozdeystviyakh. Vestnik MEI. 2016;5: 88-93

4. Kosharnaya Yu.V. Metodika analiza parametrov elektropotrebleniya dlya normirovaniya i otsenki energosberezheniya pri provedenii energoaudita predpriyatii i organizatsii. Elektrooborudovanie: ekspluatatsiya i remont. 2016;4:56-69.

5. Voevoda AE, Kharitonova DD, Val' PV. Kratkosrochnoe prognozirovanie elektropotrebleniya na osnove metoda sluchainogo lesa. Elektroenergetika glazami molodezhi - 2016 Materialy 7 Mezhdunarodnoi molodezhnoi nauchno-tekhnicheskoi konferentsii. 2016. pp. 124-127.

6. Dong Q, Xing K, Zhang H. Artificial neural network for assessment of energy consumption and cost for cross laminated timber office building in severe cold regions. Sustainability. 2017;10(1):84.

7. Firsova IA, Vasbieva DG, Kosarenko NN, et al. Energy consumption forecasting for power supply companies. International Journal of Energy Economics and Policy. 2019;9(1):1-6.

8. Popov V, Fedosenko M, Tkachenko V, et al. Forecasting consumption of electrical energy using time series comprised of uncertain data. 2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings 6. 2019. pp. 201-204.

9. Lemke F. Probabilistic energy forecasting based on self-organizing inductive modeling. Advances in Intelligent Systems and Computing. 2019;871:405-420.

10. Wang J, Zeng N, Zhou B, et al. Data center energy consumption models and energy efficient algorithms. Jisuanji Yanjiu yu Fazhan. 2019;56(8):1587-1603.

11. Jiang P, Dong J, Huang H. Forecasting china's renewable energy terminal power consumption based on empirical mode decomposition and an improved extreme learning machine optimized by a bacterial foraging algorithm. Energies. 2019;12(7):1331.

12. Runge J, Zmeureanu R. Forecasting energy use in buildings using artifici al neural networks: a review. Energies. 2019;12(17):3254.

13. Kudrin BI. Organizatsiya, postroyeniye i upravleniye energeticheskim khozyaystvom metallurgicheskikh predpriyatiy: Diss. … doktora tenkhicheskikh nauk. Gos. soyuznyy in-t po proyektirovaniyu metallurgicheskikh zavodov (GIPROMEZ). 1979. 282 s.

14. rp5.ru Raspisaniye pogody. Available at: elektronnyy resurs: https://rp5.ru/ Accessed to: May 20, 2020.

15. Zarubin O.A. Primeneniye neyronnykh setey dlya tseley analiza dannykh distantsionnogo zondirovaniya Zemli // Sovremennyye nauchnyye issledovaniya i innovatsii. 2016. N 8. Available at: URL: http://web.snauka.ru/issues/2016/08/70887. Accessed to: May 20, 2020.