NON-INSTRUTIVE LOAD MONITORING – A PROSPECTIVE DIRECTION OF BUILDING ELECTRICITY CONSUMPTION MONITORING

Abstract

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.