Simulation of Malfunctions in Home Appliances’ Power Consumption

Alexios Papaioannou (), Asimina Dimara, Christoforos Papaioannou, Ioannis Papaioannou, Stelios Krinidis, Christos-Nikolaos Anagnostopoulos (), Christos Korkas, Elias Kosmatopoulos, Dimosthenis Ioannidis and Dimitrios Tzovaras
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Alexios Papaioannou: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Asimina Dimara: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Christoforos Papaioannou: Management Science and Technology Department, Democritus University of Thrace (DUTh), 65404 Kavala, Greece
Ioannis Papaioannou: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Stelios Krinidis: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Christos-Nikolaos Anagnostopoulos: Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece
Christos Korkas: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Elias Kosmatopoulos: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Dimosthenis Ioannidis: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Dimitrios Tzovaras: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece

Energies, 2024, vol. 17, issue 17, 1-31

Abstract: Predicting errors in home appliances is crucial for maintaining the reliability and efficiency of smart homes. However, there is a significant lack of such data on appliance malfunctions that can be used in developing effective anomaly detection models. This research paper presents a novel approach for simulating errors of heterogeneous home appliance power consumption patterns. The proposed model takes normal consumption patterns as input and employs advanced algorithms to produce labeled anomalies, categorizing them based on the severity of malfunctions. One of the main objectives of this research involves developing models that can accurately reproduce anomaly power consumption patterns, highlighting anomalies related to major, minor, and specific malfunctions. The resulting dataset may serve as a valuable resource for training algorithms specifically tailored to detect and diagnose these errors in real-world scenarios. The outcomes of this research contribute significantly to the field of anomaly detection in smart home environments. The simulated datasets facilitate the development of predictive maintenance strategies, allowing for early detection and mitigation of appliance malfunctions. This proactive approach not only improves the reliability and lifespan of home appliances but also enhances energy efficiency, thereby reducing operational costs and environmental impact.

Keywords: error simulation; malfunction prediction; home appliance power consumption; anomaly detection; smart homes (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: 2024
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