Enrique Cano-Suñén

This paper addresses three indoor air quality (IAQ) challenges in large‐scale infrastructures under real operational conditions. First, methodological procedures for CO ₂ monitors: selection according to their key features, strategic placement to guarantee data quality, and calibration to ensure long‐term consistency and second, real‐time monitoring through a campus‐scale Internet of Things (IoT) ecosystem, yielding the following contributions. A monitoring map is used to comprehensively classify spaces by fulfilling international recommendations. An IAQ study highlights the combined influence of occupancy, size, and contextual factors. And an analysis of CO ₂ performance understands interactions between building operation and human behavior. Finally, for each contribution this paper proposes extrapolatable and replicable strategies to move toward data‐driven decisions for efficient IAQ management in tertiary buildings.

To meet the 2050 targets about climate change and decarbonization, accomplishing thermal comfort, Internet of Things (IoT) ecosystems are key enabling technologies to move the Built Environment (BE) towards Smart Built Environment (SBE). The first contributions of this paper conceptualise SBE from its dynamic and adaptative perspectives, considering the human habitat, and enunciate SBE as a multidimensional approach through six ways of inhabiting: defensive, projective, scientific, thermodynamic, subjective, and complex. From these premises, to analyse the performance indicators that characterise these multidisciplinary ways of inhabiting, an IoT-driven methodology is proposed: to deploy a sensor infrastructure to acquire experimental measurements; analyse data to convert them into context-aware information; and make knowledge-based decisions. Thus, this work tackles the inefficiency and high energy consumption of public buildings with the challenge of balancing energy efficiency and user comfort in dynamic scenarios. As current systems lack real-time adaptability, this work integrates an IoT-driven approach to enhance energy management and reduce discrepancies between measured temperatures and normative thresholds. Following the energy efficiency directives, the obtained results contribute to the following: understanding the complexity of the SBE by analysing its thermal performance, quantifying the potential of energy saving, and estimating its economic impact. The derived conclusions show that IoT-driven solutions allow the generation of real-data-based models on which to enhance SBE knowledge, by increasing energy efficiency and guaranteeing user comfort while minimising environmental effects and economic impact.

Advances towards smart ecosystems showcase Internet of Things (IoT) as a transversal strategy to improve energy efficiency in buildings, enhance their comfort and environmental conditions, and increase knowledge about building behavior, its relationships with users and the interconnections among themselves and the environmental and ecological context. EU estimates that 75% of the building stock is inefficient and more than 40 years old. Although many buildings have some type of system for regulating the indoor temperature, only a small subset provides integrated heating, ventilation, and air conditioning (HVAC) systems. Within that subset, only a small percentage includes smart sensors, and only a slight portion of that percentage integrates those sensors into IoT ecosystems. This work pursues two objectives. The first is to understand the built environment as a set of interconnected systems constituting a complex framework in which IoT ecosystems are key enabling technologies for improving energy efficiency and indoor air quality (IAQ) by filling the gap between theoretical simulations and real measurements. The second is to understand IoT ecosystems as cost-effective solutions for acquiring data through connected sensors, analyzing information in real time, and building knowledge to make data-driven decisions. The dataset is publicly available for third-party use to assist the scientific community in its research studies. This paper details the functional scheme of the IoT ecosystem following a three-level methodology for (1) identifying buildings (with regard to their use patterns, thermal variation, geographical orientation, etc.) to analyze their performance; (2) selecting representative spaces (according to their location, orientation, use, size, occupancy, etc.) to monitor their behavior; and (3) deploying and configuring an infrastructure with +200 geolocated wireless sensors in +100 representative spaces, collecting a dataset of +10,000 measurements every hour. The results obtained through real installations with IoT as a learning factory include several learned lessons about building complexity, energy consumption, costs, savings, IAQ and health improvement. A proof of concept of building performance prediction based on neural networks (applied to CO₂ and temperature) is proposed. This first learning shows that IAQ measurements meet recommended levels around 90% of the time and that an IoT-managed HVAC system can achieve energy-consumption savings of between 10 and 15%. In summary, in a real context involving economic restrictions, complexity, high energy costs, social vulnerability, and climate change, IoT-based strategies, as proposed in this work, offer a modular and interoperable approach, moving towards smart communities (buildings, cities, regions, etc.) by improving energy efficiency and environmental quality (indoor and outdoor) at low cost, with quick implementation, and low impact on users. Great challenges remain for growth and interconnection in IoT use, especially challenges posed by climate change and sustainability.

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.