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.

Purpose: Research focused on indoor thermal comfort has increased significantly in recent years. This is mainly due to the relationship of said variable with energy efficiency, derived from an optimal operation of conditioning systems. Related to this, it is important to highlight the relevance of the clothing insulation level, due to its connection with thermal comfort.Design/methodology/approach: The present study proposes a machine learning-based method, by applying four Machine Learning algorithms. For this purpose, the ASHRAE Global Thermal Comfort Database II was used, and nineteen features were selected as input variables.Findings: Among the four algorithms considered, Multi-Layer Perception more accurately predicted the clothing insulation level of the participants (R2=0,883). Moreover, as for the relevance of the input variables, those related to indoor and outdoor climatic conditions had the greatest effect on the estimation of the observed output.Originality/value: A Machine Learning-based approach to delve into the analysis of the clothing insulation level, as opposed to other studies that rely on regression models, is proposed. Moreover, it is not based on a single technique but carries out a systematic comparative analysis of several of the most commonly in the field of thermal comfort. Additionally, it seeks to encompass a wide range of input variables to investigate their relationship with the clothing insulation level.