Non-residential buildings contribute to around 20% of the total energy consumed in Europe. This consumption continues to increase globally. Smart building proposals (focused on Nearly Zero Energy Building (NZEB), air quality monitoring, energy saving with thermal comfort, etc.) were already necessary before 2020, and the pandemic has made this research and development area more essential. Furthermore, the need to meet the Sustainable Development Goals (SDG) and obtain technological solutions based on the Internet of Things (IoT) requires holistic contributions through real installations that serve as spaces for measuring, testing, study and research. This article proposes a “measure–analyse–decide and act” methodology to quantify the Smart Readiness Indicator (SRI) for university buildings as a reference environment for energy efficiency and COVID-19 prevention models. Two conceptual spaces (physical and digital) within two dimensions (users and infrastructures) are designated over an IoT three-level model (information acquisition, interoperable communication, and data-driven decision). An IoT ecosystem (sensoriZAR) was implemented as a proof-of-concept of a smart campus at the University of Zaragoza, Spain. Focused on CO₂ and energy consumption monitoring, the results showed effectiveness through real installations, demonstrating the IoT potential as SDG-enabling technologies. These contributions allow not only experimental lab tests (from the authors’ expertise in several specialties of Industrial, Mechanical, Design, Thermal, Electrical, Electronic, Computer and Telecommunication Engineering) but also a reference model for direct application in academic works, research projects and institutional initiatives, extendable to professional environments, buildings and cities.

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.

A novel technique of design of experiments applied to numerical simulations is proposed in this paper as a methodology for the sizing and design of thermal storage equipment integrated in any specific application. The technique is carried out through the response surfaces in order to limit the number of simulation runs required to achieve an appropriate solution. Thus, there are significant savings on the time spent on the design as well as a potential cost saving on the experimentation if similarity relationships between the prototype and the model are met. The technique is applied here to a previously developed and validated numerical model that simulates the thermal behavior of a phase change material-air heat exchanger. The incorporation of the thermal energy storage unit is analyzed in the case of a solar cooling application, improving the system coefficient of performance. The economic viability is mainly conditioned by the price of the macroencapsulated phase change material.