Journal
ENERGIES
Volume 14, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/en14030767
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
Categories
Funding
- European Regional Development Fund of the European Union
- Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation [T2EDK-03898]
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This paper presents a real-time NILM system that detects the turning-on of a target appliance and estimates its consumption in real-time by processing the measured active power transient response. The proposed system consists of three main blocks: an event detection algorithm, a convolutional neural network classifier, and a power estimation algorithm, demonstrating high computational and memory efficiency in achieving promising results in real-time.
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.
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