Abstract
The analysis of energy data of electrical devices in Smart Homes (SHs) represents an important factor for the decision-making process of energy management both from the consumer perspective by saving money and also in terms of energy redistribution and CO\(_{2}\) emissions reduction, by knowing how the energy demand of a building is composed in the Smart Grid (SG). A proactive monitoring and control mechanism motivates the need to face with the identification of appliances. In this context, the paper proposes a model for the automatic identification of electrical devices driven by 19 features that are formalized through a mathematical notation. On the basis of such proposed features, three different classifiers are trained and experimented, by evaluating their accuracy, for the identification of 33 types of appliances.
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This work was partially performed in the context of the TAKEDOWN, an EU Horizon 2020 Research and Innovation Programme, Grant Agreement no 700688.
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Tundis, A., Faizan, A., Mühlhäuser, M. (2020). Electrical Devices Identification Driven by Features and Based on Machine Learning. In: Littlewood, J., Howlett, R., Capozzoli, A., Jain, L. (eds) Sustainability in Energy and Buildings. Smart Innovation, Systems and Technologies, vol 163. Springer, Singapore. https://doi.org/10.1007/978-981-32-9868-2_18
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DOI: https://doi.org/10.1007/978-981-32-9868-2_18
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