To improve the energy efficiency and performance of buildings, it is essential to understand the factors that influence indoor thermal comfort. Through an extensive analysis of various variables, actions can be developed to enhance the thermal sensation of the occupants, promoting sustainability and economic benefits in conditioning systems. This study identifies eight key variables: indoor air temperature, mean radiant temperature, indoor globe temperature, CO₂, age, outdoor temperature, indoor humidity, and the running mean temperature, which are relevant for predicting thermal comfort in Mediterranean office buildings. The proposed methodology effectively analyses the relevance of these variables, using five techniques and two different databases, Mediterranean climate buildings published by ASHRAE and a study conducted in Seville, Spain. The results indicate that the extended database to 21 variables improves the quality of the metrics by 5%, underscoring the importance of a comprehensive approach in the analysis. Among the evaluated techniques, random forest emerges as the most successful, offering superior performance in terms of accuracy and other metrics, and this method is highlighted as a technique that can be used to assist in the design and operation or control of a building’s conditioning system or in tools that recommend adaptive measures to improve thermal comfort.

Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.

Designing a Zero Energy Building (ZEB) requires an optimal choice of the materials of a building envelope. Different material properties and window areas could be selected to generate a set of possibilities of the design of a building, being the demand defined by its thermal characteristics. The energy demand of a building could be produced with renewable systems such as photovoltaic. Moreover, the HVAC (Heating, Ventilation, and Air Conditioning) systems could be selected considering the system cost. The present methodology focuses on finding a balance between investment and low energy consumption for a building, based on an integrated optimization method. Such methodology applies a Tabu search algorithm and a simplified model to select the passive design. Afterwards, active elements of the design, as photovoltaic systems, are selected. Therefore, the methodology faces the problem of estimating the annual energy demand and the life cycle cost. The goal is the design of a building with a large amount of energy generated by renewable energy, to have a ZEB, and in the worst case, a nearly Zero Energy Building (nZEB). This methodology reduces investment, reduces the energy demand and selects the best construction materials, renewable energy, and air conditioning system. The present paper analyzes a set of case studies considering different climatic zones in Spain. The results conclude that the methodology could help builders in the design stage, to find a new design that allows a ZEB with the optimal life cycle cost.

Although different investigations have been carried out on the analysis of adaptive thermal comfort in naturally ventilated buildings, fewer have focused on mixed mode operation. Moreover, there is limited research as for the implementation of adaptive comfort models into the control system of buildings. Therefore, this paper investigates how the application of a setpoint based on adaptive comfort control (ACC) would affect occupants’ comfort considering mixed mode operation and based on the results of a longitudinal field study in an academic office building of a tertiary educational institution in southern Spain. The manuscript analyses the Thermal Preference Vote over 12 months in a mixed mode room with an HVAC system whose setpoint is adjusted with a previously calculated adaptive algorithm for the building. For that, a thorough analysis was conducted in which users identified situations regarding thermal comfort and the operation of the conditioning system was collected. The results indicate that it is possible to develop adaptive comfort models that ensure the thermal well-being of occupants. Moreover, this study highlights the need for further research to assess the implications of ACC in terms of comfort and energy consumption as well as addressing the future improvements and the limitations of the work carried out.

The objective of this study is to determine the factors that influence “business performance” or the “role of entrepreneurs”, as well as to analyse whether gender functions as a moderating variable, based on a survey conducted on a sample of 127 companies. Most of the literature in the tourism sector only considers hotel accommodation; thus, it is still limited in the case of nonhotel accommodation. Our research fills this gap by focusing on both types of establishments. An explanatory and confirmatory model has been carried out based on a PLS-SEM approach, taking the factors that determine business performance into account. Moreover, a MICOM and a multi-group analysis have been undertaken in order to check whether gender acts as a moderator. Our findings reveal that there are no significant differences between tourist accommodation companies run by women and men in Andalusia; hence, gender is not a moderating variable regarding business performance. Furthermore, as full measurement invariance has been established, a comparison between groups emphasises that the influence of the environment, and the resources and capabilities of the entrepreneur, are factors that affect women more than men, and the number of employees, work experience, and the occupancy rate are more important for businesswomen, whereas the distance to the nearest airport, check-in, and the occupancy rate are more important for businessmen. This empirical study has practical implications for hospitality industry professionals and concerned authorities which are responsible for designing strategies and policies related to this sector in Andalusia.