Cano-Suñén, E.; Martínez, I.; Fernández, Á.; Zalba, B.; Casas, R. Internet of Things (IoT) in Buildings: A Learning Factory. Sustainability2023, 15, 12219.
Cano-Suñén, E.; Martínez, I.; Fernández, Á.; Zalba, B.; Casas, R. Internet of Things (IoT) in Buildings: A Learning Factory. Sustainability 2023, 15, 12219.
Cano-Suñén, E.; Martínez, I.; Fernández, Á.; Zalba, B.; Casas, R. Internet of Things (IoT) in Buildings: A Learning Factory. Sustainability2023, 15, 12219.
Cano-Suñén, E.; Martínez, I.; Fernández, Á.; Zalba, B.; Casas, R. Internet of Things (IoT) in Buildings: A Learning Factory. Sustainability 2023, 15, 12219.
Abstract
Advances towards smart ecosystems showcases Internet of Things (IoT) as a transversal strategy to improve energy efficiency in buildings, to enhance their comfort and environmental conditions, and to grow knowledge about building behavior, its relationships with the users and the interconnections among themselves and with the environmental and ecological context. EU estimates that 75% of the building stock is inefficient and aged with +40 years old. Although many buildings have some kind of system for regulating their indoor temperature, only a small subset provides integrated Heating, Ventilation, and Air Conditioning (HVAC) systems; within that subset, only a low percentage includes smart sensors, and only a minimum of that percentage integrates those sensors into IoT ecosystems. This work proposes several contributions. On the one hand, to understand the built environment as a set of interconnected systems that constitute a complex framework where IoT ecosystems are key enabling technologies to improve energy efficiency and Indoor Air Quality (IAQ) by filling the gap between theoretical simulations and real measurements. On the other hand, to understand IoT ecosystems as cost-effective solutions where: acquire data through connected sensors, analyze information in real-time, and build knowledge to make data-driven decisions. Furthermore, data set is public for third-party use to contribute scientific community to their research studies. Thus, this paper also contributes with a detailed functional scheme of IoT ecosystem deployed in 3 buildings of University of Zaragoza (Spain) with +200 geolocated wireless sensors with +100 representative spaces. The obtained results, through real installations with IoT as learning factory, show several learned lessons (about building complexity; energy consumption, costs and savings; and IAQ and health improvement) and contribute, as a proof-of-concept, with a proposal of prediction of building performance based on both correlations (between CO2 and occupancy) and neural networks (applied to CO2 and temperature). In summary, in a real context of economic restrictions, complexity, higher energy costs, social vulnerability and climate change, IoT-based strategies, as proposed in this work, highlight as an open, modular and interoperable approach to move towards smart communities (buildings, cities, regions, etc.) by improving energy efficiency and environmental quality (indoor and outdoor) with low cost, quick implementation, and low impact on users within great challenges for growth, interconnection, climate change and sustainability.
Keywords
Internet of Things (IoT); Indoor Air Quality (IAQ); Energy efficiency; Smart buildings; Learning factory
Subject
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
