A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

Christos Athanasiadis, Dimitrios Doukas, Theofilos Papadopoulos and Antonios Chrysopoulos
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Christos Athanasiadis: Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Dimitrios Doukas: NET2GRID BV, Krystalli 4, 54630 Thessaloniki, Greece
Theofilos Papadopoulos: Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Antonios Chrysopoulos: NET2GRID BV, Krystalli 4, 54630 Thessaloniki, Greece

Energies, 2021, vol. 14, issue 3, 1-23

Abstract: Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency.

Keywords: convolutional neural network; energy consumption; energy data analytics; energy disaggregation; machine learning; non-intrusive load monitoring; real-time; smart meter data; smart meters; transient load signature (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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