Abstract
Developing efficient techniques for energy optimization and conservation requires a thorough understanding of the patterns of energy usage among different home appliances. This study looks into the energy usage patterns of several household appliances and assesses how well machine learning methods classify these patterns. Appliances are categorized into three groups: constantly on devices, program-based on-demand devices, and non-program-based on-demand devices. Key statistical features such as periodograms, mean, and standard deviation are extracted for machine learning classification, employing DenseNet 1D, XGBoost, LightGBM, SVM, KNN, and Random Forest. The findings show DenseNet and LightGBM performing exceptionally well, with nearly 98% accuracy in classifying constantly-on and program-based devices, indicating their potential in optimizing energy usage.
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This work is partially supported by SMART2B https://smart2b-project.eu/ project funded by the European Union’s Horizon 2020 under Grant Agreement 101023666.
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Papaioannou, A. et al. (2024). An Innovative Methodology for Revealing Home Appliances’ Consumption Patterns to Transform Energy Management and Maintenance Strategies. In: Maglogiannis, I., Iliadis, L., Karydis, I., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024 IFIP WG 12.5 International Workshops. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-63227-3_27
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DOI: https://doi.org/10.1007/978-3-031-63227-3_27
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