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	Communications in Computer and Information Science
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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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.), 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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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), Communications in Computer and Information Science: 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.
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).

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

TY - GEN

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.

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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U2 - 10.1007/978-3-032-19019-2_2

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

M3 - Contribución a la conferencia

AN - SCOPUS:105036673698

SN - 978-3-032-19018-5

VL - 2742

T3 - Communications in Computer and Information Science

SP - 15

EP - 29

BT - Communications in Computer and Information Science

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, 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