This paper presents a review of technologies under the paradigm 4.0 applied to the study of the thermal comfort and, implicitly, energy efficiency. The research is based on the analysis of the Internet of Things (IoT) literature, presenting a comparison among several approaches adopted. The central objective of the research is to outline the path that has been taken throughout the last decade towards a people-centric approach, discussing how users switched from being passive receivers of IoT services to being an active part of it. Basing on existing studies, authors performed what was a necessary and unprecedented grouping of the IoT applications to the thermal comfort into three categories: the thermal comfort studies with IoT hardware, in which the approach focuses on physical devices, the mimicking of IoT sensors and comfort using Building Simulation Models, based on the dynamic modelling of the thermal comfort through IoT systems, and Crowdsensing, a new concept in which people can express their sensation proactively using IoT devices. Analysing the trends of the three categories, the results showed that Crowdsensing has a promising future in the investigation through the IoT, although some technical steps forward are needed to achieve a satisfactory application to the thermal comfort matter.

The building sector is responsible for a significant amount of energy consumption and greenhouse gas (GHG) emissions. Thus, the monitoring, control and optimization of energy consumption in buildings will play a critical role in the coming years in improving energy efficiency in the building sector and in reducing greenhouse gas emissions. However, while there are a significant number of studies on how to make buildings smarter and manage energy through smart devices, there is a need for more research on integrating buildings with legacy equipment and systems. It is therefore vital to define mechanisms to improve the use of energy efficiency in existing buildings. This study proposes a new architecture (PHOENIX architecture) for integrating legacy building systems into scalable energy management systems with focus also on user comfort in the concept of interoperability layers. This interoperable and intelligent architecture relies on Artificial Intelligence/Machine Learning (AI/ML) and Internet of Things (IoT) technologies to increase building efficiency, grid flexibility and occupant well-being. To validate the architecture and demonstrate the impact and replication potential of the proposed solution, five demonstration pilots have been utilized across Europe. As a result, by implementing the proposed architecture in the pilot sites, 30 apartments and four commercial buildings with more than 400 devices have been integrated into the architecture and have been communicating successfully. In addition, six Trials were performed in a commercial building and five key performance indicators (KPIs) were measured in order to evaluate the robust operation of the architecture. Work is still ongoing for the trials and the KPIs’ analysis after the implementation of PHOENIX architecture at the rest of the pilot sites.

The arrival of the Internet of Things (IoT) paradigm has opened the door to a variety of services for building users. Considering the long-lasting issue of high energy use by buildings and low-energy literacy, it is tempting to use this new technology for increasing the literacy of users. This paper shows the results of a study performed in two pilot buildings with real users that have interacted with a series of energy educational interventions that encourage them in a timed and personalised way to reduce their energy consumption. The interventions aimed at reducing the consumption of energy and a close follow-up of the intervention from a behavioural aspect has been performed. The results show that the users, when interacting with the intervention and staying active, can reduce the energy consumption in the building by more than 30%, but the average savings are of 20%. This is in consensus with the literature, but in our case, the intervention has been one showing that personalised methods can result in energy reductions as large as those of more standard interventions.

Ahead of the energy crisis, several countermeasures to reduce the energy demand require an active change in the end users’ energy patterns. There are strategies known as demand response (DR) programs that have been tested in recent years, and they have showed us that users’ behaviours can considerably reduce their effectiveness. This is due to a lack of sufficient knowledge, which leads to a lack of acceptance and participation. The key aim of this research is to detect which aspects influence acceptance of energy flexibility strategies the most. Through a series of tools, such as direct questionnaires, brief and user-friendly explanations, and analysis of variance, these aspects are studied by delving into specific topics such as smart home technologies, change in habits and patterns of energy use, and DR programs. In the literature, these topics have been studied separately, but they all contribute to the global acceptance: a comprehensive vision of the matter is the novelty of this work. The key findings are encouraging: 72% of the respondents demonstrated their willingness to enrol in a DR program. A reluctance to change habits was shown, in particular, among the age range 26–40, while women were more environmentally aware and more likely to participate in energy flexibility strategies. The modality of direct load control (which gives control to the utility company) is confirmed to be harder to be accepted (from 13 to 27% less acceptance depending on the category), with people who share a flat being the most likely to try it, and people who live with their parents being the less inclined ones. Acceptance increased when we provided plain language explanations, as seen in the case of smart home technologies: 97% of people who never tried them were declared to be willing to test smart technologies after a simple elucidation that was included in the questionnaire, showing that a right approach to the users led to a greater show of interest. This research highlights that the users’ background and demographics characteristics (namely age, gender, educational level, home situation, home tenure, presence of children, and average income) should be taken into account when it comes to designing new energy flexibility strategies, since differences in the acceptance among groups have been found. The work also presents insights on the payback periods of legacy equipment in the EU energy context, demonstrating that a timely intervention can require half the time compared to that of the period prior to the crisis.

In this research, we have created a comprehensive Internet of Things (IoT) framework that allows for better communication between users and machines of the building. With this, users are able to express their thermal preferences so that the connected air conditioning machine could adjust automatically to the needs. In addition, people will be able to understand the conditioning operation through representations of augmented reality, closing in this way the loop of communication. The technology is highly interesting as its cost is virtually null in users with a smart-phone and an air conditioning machine connected to the Internet (as is becoming the norm). The paper shows a methodology consisting of interpreting the will of the occupants with respect to thermal comfort by an IoT platform. The paper shows several simulations performed to evaluate what would happen in a scenario of that kind. The results have shown that the IoT platform allows everybody to have their say in the comfort temperature and, more importantly, shows that the regulation following this path has to be done in a way in which over-compensation for cold or hot periods is not generated for the votes of the occupants. Overall, the system seems highly promising, and is capable of minimizing the dissatisfaction of the occupants in short times.

Buildings have now adopted a new dimension: the dimension of smartness. The rapid arrival of connected devices, together with the smart features that they provide, has allowed for the transition of existing buildings towards smart buildings. The assessment of the smartness of the large number of existing buildings could exhaust resources, but some organisations are requesting this regardless (such as the smart readiness indicator of the European Union). To tackle this issue, this work describes a tool that was created to find connected devices to automatically evaluate smartness. The tool, which was given the name SmartWatcher, uses a design-for-purpose natural language processing algorithm that converts verbal information into numerical information. The method was tested on real buildings in four different geographical locations. SmartWatcher is shown to be powerful, as it was capable of obtaining numerical values from verbal descriptions of devices. Additionally, a preliminary comparison of values obtained using the automatic engine and clipboard assessments showed that although the results were still far from being perfect, some visual correlation could be seen. This anticipates that, with the addition of appropriate techniques that refine this algorithm, or with the addition of new ones (with other more advanced natural language processing methods), the accuracy of this tool could be greatly increased.