Smart Built Environment (SBE): a challenge for Internet of Things (IoT) ecosystems to understand dynamic habitats and their users as complex systems
Martínez, Ignacio (Universidad de Zaragoza) ; Cano-Suñén, Enrique (Universidad de Zaragoza) ; Casas, Roberto (Universidad de Zaragoza) ; Fernández, Ángel (Universidad de Zaragoza)
Resumen: Accurately modelling building performance remains a major challenge, particularly when aiming to implement predictive control strategies in digital twin systems. The gap between simulated and real building behaviour –often over 30 %– is largely due to the unpredictable nature of human activity and the context-dependent variability of indoor conditions. Beyond energy use, gap between real and controlled comfort is often observed when operation reduces comfort to fixed setpoints. To reduce these gaps, it is key to incorporate in-situ data reflecting how buildings are actually used, occupied, and affected by environmental and operational variables.
This paper contributes with an Internet of Things (IoT)-based methodology focused on identifying and analysing key observable fields within Smart Built Environment (SBE). These fields include occupancy patterns, indoor environmental conditions, external climate, architectural configuration, energy flows, system efficiency, and operational cost. This SBE-IoT framework is structured through levels (data, information, knowledge, and cognitive control) and layers (acquisition, ingestion, processing, storage, analysis, understanding and decision-making). From this framework, data are collected and processed through a deployed IoT ecosystem (sensoriZAR) that integrates: real-time acquisition, pattern detection, and multicriteria analysis for supporting Digital Twin (DT) approaches through decision-making and informed-actuation.
The proposed SBE-IoT framework was tested in a real-world application to a university building, representative of a smart campus. The experimental results of the cross-relation study have identified comfort oversupply periods (temperatures >21 °C) lasting 30 h/month, causing 7.9 MWh in energy use −45 % of Heating, Ventilation and Air Conditioning (HVAC) demand– and €500/month in costs. This approach proved to be fully operational, low-cost (∼€3000), and rapidly scalable (Return On Investment, ROI <6 months). This SBE-IoT framework enhances digital twin capabilities by providing empirical, context-specific insight to support adaptive, efficient, and replicable decision-making in smart environments.
Idioma: Inglés
DOI: 10.1016/j.jobe.2025.114620
Año: 2025
Publicado en: Journal of Building Engineering 116 (2025), 114620 [22 pp.]
ISSN: 2352-7102
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Construc. Arquitectónicas (Dpto. Arquitectura)
Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
Área (Departamento): Área Ingeniería Mecánica (Dpto. Ingeniería Mecánica)
Área (Departamento): Área Ingeniería Telemática (Dpto. Ingeniería Electrón.Com.)
Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace.
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Exportado de SIDERAL (2025-12-04-14:39:50)
This paper contributes with an Internet of Things (IoT)-based methodology focused on identifying and analysing key observable fields within Smart Built Environment (SBE). These fields include occupancy patterns, indoor environmental conditions, external climate, architectural configuration, energy flows, system efficiency, and operational cost. This SBE-IoT framework is structured through levels (data, information, knowledge, and cognitive control) and layers (acquisition, ingestion, processing, storage, analysis, understanding and decision-making). From this framework, data are collected and processed through a deployed IoT ecosystem (sensoriZAR) that integrates: real-time acquisition, pattern detection, and multicriteria analysis for supporting Digital Twin (DT) approaches through decision-making and informed-actuation.
The proposed SBE-IoT framework was tested in a real-world application to a university building, representative of a smart campus. The experimental results of the cross-relation study have identified comfort oversupply periods (temperatures >21 °C) lasting 30 h/month, causing 7.9 MWh in energy use −45 % of Heating, Ventilation and Air Conditioning (HVAC) demand– and €500/month in costs. This approach proved to be fully operational, low-cost (∼€3000), and rapidly scalable (Return On Investment, ROI <6 months). This SBE-IoT framework enhances digital twin capabilities by providing empirical, context-specific insight to support adaptive, efficient, and replicable decision-making in smart environments.
Idioma: Inglés
DOI: 10.1016/j.jobe.2025.114620
Año: 2025
Publicado en: Journal of Building Engineering 116 (2025), 114620 [22 pp.]
ISSN: 2352-7102
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Construc. Arquitectónicas (Dpto. Arquitectura)
Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
Área (Departamento): Área Ingeniería Mecánica (Dpto. Ingeniería Mecánica)
Área (Departamento): Área Ingeniería Telemática (Dpto. Ingeniería Electrón.Com.)
Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace. 0
Exportado de SIDERAL (2025-12-04-14:39:50)
Este artículo se encuentra en las siguientes colecciones:
Artículos > Artículos por área > Construcciones Arquitectónicas
Artículos > Artículos por área > Tecnología Electrónica
Artículos > Artículos por área > Ingenieria Telematica
Artículos > Artículos por área > Ingeniería Mecánica
Registro creado el 2025-12-04, última modificación el 2025-12-04