A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks
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A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks Yassine Himeur1
· Abdullah Alsalemi1 · Faycal Bensaali1 · Abbes Amira2
Received: 28 January 2020 / Accepted: 21 August 2020 © The Author(s) 2020
Abstract Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micromoment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy. Keywords Energy consumption · Micro-moments · Deep neural network · Anomalies detection · Visualization · Energy efficiency
Introduction Most of today’s end-user behaviors provoke a high energy cost from keeping the room lights on to watching TV all Yassine Himeur
[email protected] Abdullah Alsalemi [email protected] Faycal Bensaali [email protected] Abbes Amira [email protected] 1
Department of Electrical Engineering, Qatar University, Doha, Qatar
2
Institute of Artificial Intelligence, De Montfort University, Leicester, UK
the day long, for instance. Undoubtedly, energy efficiency and green buildings have been getting an increased amount of attention in many countries in the last few years [1]. In short, it is the practice of minimizing energy usage without enduring a loss in quality. For many motives, end-users have to support the green energy industry from the first flip on light to the last push on the start button of computers [2, 3]. In fact, energy efficiency can enhance the manner buildings consume power in order to diminish detrimental effects on society, economy, and global environment [4]. In order to preserve wha
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