Smart monitoring, electric power system, intelligent meter, building electricity consumption, non-intrusive load monitoring, energy efficiency, demand management, privacy.


It is shown that in order to adapt to changing models of energy supply and increase reliability, utilities need intelligent monitoring of power grids in order to track dynamic operating conditions in distribution networks. Smart monitoring can provide utilities with a detailed description of consumer habits and maximize user awareness of consumption, leading to behavioural change and smoothing global energy demand. The driving factors of the smart home monitoring and security market are given. An increasing number of internet users, the rapid proliferation of smartphones and smart gadgets, and growing concern about remote monitoring of homes have been identified as key factors contributing to the growth of the smart home security market. It is shown that the Smart monitoring methodology on the example of a building reflects the goals of both actual monitoring and control: to reduce the energy consumption of buildings and/or to reduce electricity bills for residents; to offer grid managers more tools to better manage the growing demand and possible interruptions in energy production due to the growing integration of RES into the grid.

Non-intrusive load monitoring (NILM) is analysed – a method of analysing data on the total electrical load, obtained by measuring the current and voltage at one point, followed by the division of the total load into a load of individual devices, which can play a key role in the digital transition in the electric power industry. This technology is able not only to improve the current operational activities of electricity companies but also to form the basis of the formation of new relations between subjects of energy markets. NILM technology has seen significant success thanks to advances in machine learning, signal processing, and pattern recognition. It was determined that the fundamental stages of a typical NILM structure are data collection, feature extraction, signal decomposition, and device identification.


Кириленко О.В., Денисюк С.П., Блінов І.В. Цифрова трансформація енергетики: сучасні тенденції та завдання. Праці Інституту електродинаміки НАН України, Вип. 65, Серпень 2023, С. 5–14.

Денисюк С.П., Бєлоха Г.С., Чернищук І.С., Лисий В.В. Світові тенденції впровадження відновлюваних джерел енергії та особливості їх реалізації при відновленні економіки України. Енергетика: економіка, технології, екологія. 2022. № 4. С. 7–23.

Денисюк С.П., Мельничук Г.В., Чернищук І.С., Лисий В.В. Техніко-економічні механізми розвитку локальних систем енергозабезпечення (Microgrid). Енергетика: економіка, технології, екологія. 2021. № 4. С. 7–22.

Stanimirovic A., Bogdanovic´ M., Frtunic´ M., Stoimenov L. Low-voltage electricity network monitoring system: Design and production experience. International Journal of Distributed Sensor Networks, 2020, Vol. 16(1).

Pawar J.P., Amirthaganesh S., Arun Kumar S., Satiesh Kumar B. Real time energy measurement using smart meter. 2016 Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, India, 2016, pp. 1-5.

Girija R., Pavan R., Trupthi C., Mouna R., Jaishma Kumari B. Design of Smart Energy Meter for Intelligent Energy Network. 2020 IJRTI, Volume 5, Issue 2, Pp. 171–175.

Dario De Santis, Domenico Aldo Giampetruzzi, Gaetano Abbatantuono, Massimo La Scala. Smart metering for low voltage electrical distribution system using Arduino. 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), Italy, 2016.

Balamurugan S., Saravanakamalam D. Energy monitoring and management using internet of things. 2017 International Conference on Power and Embedded Drive Control (ICPEDC), March 2017.

A-Guide-to-Smart-Metering utm_medium =031

Kaustav Basu. classification techniques for non-intrusive load monitoring and prediction of residential loads. Electric power. Université de Grenoble, 2014. English. – NNT: 2014GRENT089.

Basu K., Debusschere V., Bacha S. Non Intrusive Load Monitoring: A Temporal Multi-Label Classification Approach, IEEE transaction on Industrial Informatics.

Çimen, H.; Çetinkaya, N.; Vasquez, J.C.; Guerrero, J.M. A Microgrid Energy Management System Based on NonIntrusive Load Monitoring via Multitask Learning. IEEE Trans. Smart Grid 2021, 12, 977–987.

Zhang C., Zhong M., Wang Z., Goddard N., Sutton C. Sequence-to-point learning with neural networks for nonintrusive load monitoring. In Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, 2–7 February 2018; pp. 2604–2611.

Somchai Biansoongnern, Boonyang Plungklang. Non-Intrusive Appliances Load Monitoring (NILM) for Energy Conservation in Household with Low Sampling Rate. 2016 International Electrical Engineering Congress, iEECON2016, 2-4 March 2016, Chiang Mai, Thailand. Procedia Computer Science 86 ( 2016 ). P.172 – 175.

Purna Prakash K., Pavan Kumar Y.V. Systematic Statistical Analysis to Ascertain the Missing Data Patterns in Energy Consumption Data of Smart Homes. International Journal Of Renewable Energy Research. Vol.12, No.3, September, 2022. P. 1560-1573.