Smart Meter Based on Demand Forecasting for Real-Time Architecture

Dario Javier Benavides Padilla; William Paul  Arévalo Cordero; Alberto Ríos Villacorta; Leonardo Torres Valverde; Danny Vinicio Ochoa Correa

doi:10.1007/978-3-032-19019-2_2

Smart Meter Based on Demand Forecasting for Real-Time Architecture

Dario Javier Benavides Padilla (Primer Autor)
, William Paul Arévalo Cordero
, Alberto Ríos Villacorta
, Leonardo Torres Valverde
, Danny Vinicio Ochoa Correa (Último Autor)

Universidad Técnica de Ambato
Universidad de Jaen
University of Jaén

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

The transition towards smart and sustainable cities is emerging as a fundamental aspect in the future of our society. In this context, this article proposes a design for a low-cost smart energy meter that allows recording of electrical parameters for user energy monitoring. Through data training, data are validated in real-time for the execution of predictive models. The methodology is based on a measurement architecture and real-time data recording, processing, and validation. The implementation of Wide Neural Networks results in optimal demand forecasting. It is analyzed in a case study with real-life profiles at specific measurement points. The results show that the implementation of these measurements and real-time forecasting enables monitoring and supervision of energy systems from different locations.

Idioma original	Inglés
Título de la publicación alojada	Communications in Computer and Information Science
Subtítulo de la publicación alojada	Communications in Computer and Information Science
Editores	Sergio Nesmachnow, Luis Hernández Callejo
Lugar de publicación	Puebla, México
Editorial	Springer Nature
Páginas	15-29
Número de páginas	15
Volumen	2742
Edición	Primera
ISBN (versión digital)	978-3-032-19019-2
ISBN (versión impresa)	978-3-032-19018-5
DOI	https://doi.org/10.1007/978-3-032-19019-2_2
Estado	Publicada - 1 abr 2026
Evento	8th Ibero-American Congress on Smart Cities, ICSC-Cities 2025 - Puebla, México Duración: 10 nov 2025 → 12 nov 2025

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	2742 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	8th Ibero-American Congress on Smart Cities, ICSC-Cities 2025
País/Territorio	México
Ciudad	Puebla
Período	10/11/25 → 12/11/25

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

Palabras clave

Demand forecasting
Management
Real time
Smart energy meter

Acceder al documento

10.1007/978-3-032-19019-2_2

Otros archivos y enlaces

Citar esto

Benavides Padilla, D. J., Arévalo Cordero, W. P., Ríos Villacorta, A., Torres Valverde, L., & Ochoa Correa, D. V. (2026). Smart Meter Based on Demand Forecasting for Real-Time Architecture. En S. Nesmachnow, & L. Hernández Callejo (Eds.), Communications in Computer and Information Science: Communications in Computer and Information Science (Primera ed., Vol. 2742, pp. 15-29). (Communications in Computer and Information Science; Vol. 2742 CCIS). Springer Nature. https://doi.org/10.1007/978-3-032-19019-2_2

Benavides Padilla, Dario Javier ; Arévalo Cordero, William Paul ; Ríos Villacorta, Alberto et al. / Smart Meter Based on Demand Forecasting for Real-Time Architecture. Communications in Computer and Information Science: Communications in Computer and Information Science . editor / Sergio Nesmachnow ; Luis Hernández Callejo. Vol. 2742 Primera. ed. Puebla, México : Springer Nature, 2026. pp. 15-29 (Communications in Computer and Information Science).

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abstract = "The transition towards smart and sustainable cities is emerging as a fundamental aspect in the future of our society. In this context, this article proposes a design for a low-cost smart energy meter that allows recording of electrical parameters for user energy monitoring. Through data training, data are validated in real-time for the execution of predictive models. The methodology is based on a measurement architecture and real-time data recording, processing, and validation. The implementation of Wide Neural Networks results in optimal demand forecasting. It is analyzed in a case study with real-life profiles at specific measurement points. The results show that the implementation of these measurements and real-time forecasting enables monitoring and supervision of energy systems from different locations.",

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Benavides Padilla, DJ, Arévalo Cordero, WP, Ríos Villacorta, A, Torres Valverde, L & Ochoa Correa, DV 2026, Smart Meter Based on Demand Forecasting for Real-Time Architecture. En S Nesmachnow & L Hernández Callejo (eds.), Communications in Computer and Information Science: Communications in Computer and Information Science . Primera ed., vol. 2742, Communications in Computer and Information Science, vol. 2742 CCIS, Springer Nature, Puebla, México, pp. 15-29, 8th Ibero-American Congress on Smart Cities, ICSC-Cities 2025, Puebla, México, 10/11/25. https://doi.org/10.1007/978-3-032-19019-2_2

Smart Meter Based on Demand Forecasting for Real-Time Architecture. / Benavides Padilla, Dario Javier (Primer Autor); Arévalo Cordero, William Paul ; Ríos Villacorta, Alberto et al.
Communications in Computer and Information Science: Communications in Computer and Information Science . ed. / Sergio Nesmachnow; Luis Hernández Callejo. Vol. 2742 Primera. ed. Puebla, México: Springer Nature, 2026. p. 15-29 (Communications in Computer and Information Science; Vol. 2742 CCIS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Smart Meter Based on Demand Forecasting for Real-Time Architecture

AU - Benavides Padilla, Dario Javier

AU - Arévalo Cordero, William Paul

AU - Ríos Villacorta, Alberto

AU - Torres Valverde, Leonardo

AU - Ochoa Correa, Danny Vinicio

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026/4/1

Y1 - 2026/4/1

N2 - The transition towards smart and sustainable cities is emerging as a fundamental aspect in the future of our society. In this context, this article proposes a design for a low-cost smart energy meter that allows recording of electrical parameters for user energy monitoring. Through data training, data are validated in real-time for the execution of predictive models. The methodology is based on a measurement architecture and real-time data recording, processing, and validation. The implementation of Wide Neural Networks results in optimal demand forecasting. It is analyzed in a case study with real-life profiles at specific measurement points. The results show that the implementation of these measurements and real-time forecasting enables monitoring and supervision of energy systems from different locations.

AB - The transition towards smart and sustainable cities is emerging as a fundamental aspect in the future of our society. In this context, this article proposes a design for a low-cost smart energy meter that allows recording of electrical parameters for user energy monitoring. Through data training, data are validated in real-time for the execution of predictive models. The methodology is based on a measurement architecture and real-time data recording, processing, and validation. The implementation of Wide Neural Networks results in optimal demand forecasting. It is analyzed in a case study with real-life profiles at specific measurement points. The results show that the implementation of these measurements and real-time forecasting enables monitoring and supervision of energy systems from different locations.

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Y2 - 10 November 2025 through 12 November 2025

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Benavides Padilla DJ, Arévalo Cordero WP, Ríos Villacorta A, Torres Valverde L, Ochoa Correa DV. Smart Meter Based on Demand Forecasting for Real-Time Architecture. En Nesmachnow S, Hernández Callejo L, editores, Communications in Computer and Information Science: Communications in Computer and Information Science . Primera ed. Vol. 2742. Puebla, México: Springer Nature. 2026. p. 15-29. (Communications in Computer and Information Science). doi: 10.1007/978-3-032-19019-2_2