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 (First Author)
, William Paul Arévalo Cordero
, Alberto Ríos Villacorta
, Leonardo Torres Valverde
, Danny Vinicio Ochoa Correa (Last Author)

Universidad Técnica de Ambato
Universidad de Jaen
University of Jaén

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Smart Cities - 8th Ibero-American Congress, ICSC-Cities 2025
Subtitle of host publication	Communications in Computer and Information Science
Editors	Sergio Nesmachnow, Luis Hernández Callejo
Place of Publication	Puebla, México
Publisher	Springer Nature
Pages	15-29
Number of pages	15
Volume	2742
Edition	Primera
ISBN (Electronic)	978-3-032-19019-2
ISBN (Print)	978-3-032-19018-5
DOIs	https://doi.org/10.1007/978-3-032-19019-2_2
State	Published - 1 Apr 2026
Event	8th Ibero-American Congress on Smart Cities, ICSC-Cities 2025 - Puebla, Mexico Duration: 10 Nov 2025 → 12 Nov 2025

Publication series

Name	Communications in Computer and Information Science
Volume	2742 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	8th Ibero-American Congress on Smart Cities, ICSC-Cities 2025
Country/Territory	Mexico
City	Puebla
Period	10/11/25 → 12/11/25

Keywords

demand forecasting
management
real time
smart energy meter

Access to Document

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

Cite this

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. In S. Nesmachnow, & L. Hernández Callejo (Eds.), Smart Cities - 8th Ibero-American Congress, ICSC-Cities 2025 : 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. Smart Cities - 8th Ibero-American Congress, ICSC-Cities 2025 : 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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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. in S Nesmachnow & L Hernández Callejo (eds), Smart Cities - 8th Ibero-American Congress, ICSC-Cities 2025 : Communications in Computer and Information Science . Primera edn, 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, Mexico, 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 (First Author); Arévalo Cordero, William Paul ; Ríos Villacorta, Alberto et al.
Smart Cities - 8th Ibero-American Congress, ICSC-Cities 2025 : 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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 - he 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 - he 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.

KW - demand forecasting

KW - management

KW - real time

KW - smart energy meter

KW - Demand forecasting

KW - Management

KW - Real time

KW - Smart energy meter

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T3 - Communications in Computer and Information Science

SP - 15

EP - 29

BT - Smart Cities - 8th Ibero-American Congress, ICSC-Cities 2025

A2 - Nesmachnow, Sergio

A2 - Hernández Callejo, Luis

PB - Springer Nature

CY - Puebla, México

T2 - 8th Ibero-American Congress on Smart Cities, ICSC-Cities 2025

Y2 - 10 November 2025 through 12 November 2025

ER -

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. In Nesmachnow S, Hernández Callejo L, editors, Smart Cities - 8th Ibero-American Congress, ICSC-Cities 2025 : 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