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 - 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
UR - https://www.scopus.com/pages/publications/105036673698
UR - https://link.springer.com/chapter/10.1007/978-3-032-19019-2_2
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 - 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 -