Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning

Fernando Ulloa-Vásquez, Victor Heredia-Figueroa (), Cristóbal Espinoza-Iriarte, José Tobar-Ríos, Fernanda Aguayo-Reyes, Dante Carrizo and Luis García-Santander
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Fernando Ulloa-Vásquez: Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800002, Chile
Victor Heredia-Figueroa: Programa de Investigación en Radiocomunicación Digital, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800022, Chile
Cristóbal Espinoza-Iriarte: Programa de Investigación en Radiocomunicación Digital, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800022, Chile
José Tobar-Ríos: Programa de Investigación en Radiocomunicación Digital, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800022, Chile
Fernanda Aguayo-Reyes: Programa de Investigación en Radiocomunicación Digital, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Santiago de Chile 7800022, Chile
Dante Carrizo: Departamento Ing. Informatica y Cs. de la Computación, Facultad de Ingeniería, Universidad de Atacama, Copiapó 1531772, Chile
Luis García-Santander: Departamento de Ingeniería Eléctrica, Universidad de Concepción, Concepción 4089100, Chile

Energies, 2024, vol. 17, issue 6, 1-19

Abstract: The growing demand for electricity and the constant increase in electricity rates have intensified the interest of residential and non-residential energy consumers to reduce their energy consumption. The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demands new proposals to obtain a home energy management system (HEMS), which allows reducing the use of electrical energy. This article incorporates artificial intelligence techniques to demand response, allowing control, switching, turning on and off of appliances, modifying and reducing consumption, and achieving improvements in the quality of life in the home. In addition, an architecture based on a smart socket and an artificial intelligence model that recognizes the consumption of electrical appliances in high resolution (sampling every 10 s) is proposed. The system uses the Wi-Fi communication protocol, ensuring that the smart sockets wirelessly provide the data obtained to the public cloud. The use of Deep Learning allows us to obtain a central control model of the home, which, when interconnected to the smart electrical distribution networks of companies, could generate a positive impact on the environmental effects and CO 2 reduction.

Keywords: deep learning; AMR; smart-meter; smart-socket; HEMS; smart-cities; ILM (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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