Intelligent electrical pattern recognition of appliances consumption for home energy management using high resolution measurement



Automatic Meter Reading, Home Energy Management Systems, Intrusive Load Monitoring, Pattern Recognition, Smart Socket


For an efficient energy management by residential users, monitoring and control of connected household appliances is required. If the consumption pattern of each of these devices is identified, then the management will be more efficient, reducing both the billing and the CO2 emissions. This paper proposes a model for the recognition of energy consumption patterns in household appliances, based on the capture of electrical parameters in connected appliances, through Smart Socket with an Intrusive Load Monitoring approach. The data acquisition system corresponds to an-Internet of Things (IoT) platform that uses Automatic Meter Reading devices, connected to a IoT-gateway via Wi-Fi to send data to an application on the web. For the recognition of the patterns, machine learning techniques are used. Accuracy results on pattern identification are obtained about 91% after applying a backpropagation method in an Artificial Neural Network in time basis. Through this work, the prediction of consumer categories in household appliances, with high levels of reliability and under multiple operating states, is reached. These results enhance the efficient management of energy in a Smart Home and Smart Cities environment


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Author Biographies

Fernando Ulloa-Vásquez, Universidad Tecnológica Metropolitana

Fernando Ulloa Vásquez recibió su título de Ingeniero Electrónico de la Universidad Tecnológica Metropolitana (UTEM), Chile, en 1992, y de PhD. Ingeniería en Telecomunicaciones por la Universidad Politécnica de Cataluña en 2003. Es Profesor Titular en el Departamento de Electricidad de la UTEM. Sus áreas de Investigación son las aplicaciones de Sistemas de radio aeronáuticos y de canal para plataformas estratosféricas HAAP de banda ancha digital Terrestre.

Luis García-Santander, Universidad de Concepción

Luis García Santander recibió su título de Ingeniero Civil Eléctrico de la Universidad de Concepción, Chile, en 1995 y en 2003 su grado de Doctor en Ingeniería Eléctrica de la l’École Supérieure d’Électricité (Supélec) – Université Pierre et Marie Curie Paris VI, Paris-Francia. Es Profesor Asociado en el Departamento de Ingeniería Eléctrica en la Universidad de Concepción, Chile. Sus áreas de interés son la optimización de Sistemas Eléctricos, Gestión Eficiente de la Energía, Redes Inteligentes y Energías Renovables.

Dante Carrizo, Universidad de Atacama

Dante Carrizo Moreno recibió su título de Ingeniero Civil Informático de la Universidad de Concepción, Chile. Es M.Sc. y Ph.D. en Ingeniería en Software de la Universidad Politécnica de Madrid, España. Es Profesor Titular y decano de la Facultad de Ingeniería de la Universidad de Atacama, Chile. Sus áreas de interés son Ingeniería de Software, Big Data y Minería de Datos. Ha sido revisor de artículos para diversas revistas científicas y conferencias internacionales de su disciplina.

Victor Heredia-Figueroa, Universidad Tecnológica Metropolitana

Víctor Heredia Figueroa recibió su grado académico de Licenciado en Ciencias de la Ingeniería el año 2018 en la Universidad Tecnológica Metropolitana (UTEM) y es Ingeniero Civil en Computación (e) de la misma casa de estudios. Víctor desde el año 2017 a la fecha se desempeña como investigador junior en el Programa de Investigación en Radiocomunicación Digital y sus áreas de investigación/interés son ciencias de la computación, gestión de TI, radiocomunicación, IoT y vehículos autónomos.


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How to Cite

Ulloa-Vásquez, F., García-Santander, L., Carrizo, D., & Heredia-Figueroa, V. (2021). Intelligent electrical pattern recognition of appliances consumption for home energy management using high resolution measurement. IEEE Latin America Transactions, 20(2), 326–334. Retrieved from

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