Anomaly detection framework for IoT-enabled appliances using machine learning
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10586-024-04461-z.pdf
Reference28 articles.
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3. Lazzari, F., Mor, G., Cipriano, J., Gabaldon, E., Grillone, B., Chemisana, Daniel, Solsona, Francesc: User behaviour models to forecast electricity consumption of residential customers based on smart metering data. Energy Rep. 8, 3680–3691 (2022)
4. Paydin-Özkan, H.: Appliance-level anomaly detection by using control charts and artificial neural networks with power profiles. Sens. (Basel, Switzerl.) 22(17), 6639 (2022)
5. Dutta, N., Kaliannan, P., Shanmugam, P.: Application of machine learning for inter-turn fault detection in pumping system. Sci. Rep. 12(1), 1–18 (2022)
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