The estimation of indoor thermal comfort and the associated occupant feedback in office buildings is important to provide satisfactory and safe working environments, enhance the productivity of personnel, and to reduce complaints. The assessment of thermal comfort is a difficult task due to many environmental, physiological, and cultural variables that influence occupants’ thermal perception and the way they judge their working environment. Traditional physics-based methods for evaluating thermal comfort have shown shortcomings when compared to actual responses from the occupants due to the incapacity of these methods to incorporate information of various natures. In this paper, a hybrid approach based on machine learning and building dynamic simulation is presented for the prediction of indoor thermal comfort feedback in an office building in Le Bour-get-du-Lac, Chambéry, France. The office was equipped with Internet of Things (IoT) environmental sensors. Occupant feedback on thermal comfort was collected during an experimental campaign. A calibrated building energy model was created for the building. Various machine learning models were trained using information from the occupants, environmental data, and data extracted from the calibrated dynamic simulation model for the prediction of thermal comfort votes. When compared to traditional predictive approaches, the proposed method shows an increase in accuracy of about 25%.

This paper presents a methodology for parameter estimation of a suitable model for energy management services in office and apartment settings. The objective of this work is to identify model structures and tune parameters to fit recorded data. Once these parameters have been identified , the model will support energy services (prediction, explanation). Greybox models are proposed to estimate the temperature and CO 2 concentration based only on few sensors. Then, two different estimation parameter methods have been applied relying on a descent algorithm or a genetic one. Different structures have been compared. Finally, the resulting model has been applied to two case studies: an office and an apartment.

In summer time, thermal inertia appears as a passive solution to improve the thermal comfort in many part of Europe. The objective of this paper is in a first step to generate thermal comfort data from energy efficient houses in summer time with a focus on the impact of thermal inertia and natural night ventilation. In a second step, numerical simulation are run on these configurations to compare their construction systems. This work confirms the positive impact of both thermal inertia and natural night ventilation on the thermal comfort for energy efficient buildings in continental climate. It also allows to quantify the difference of indoor air temperature on this case study and to ensure the reliability of the numerical models. Finally, contrary to what is mentioned in many papers, no significant time lags between the heavy and the light weight envelope have been highlighted.

In this paper, an innovative hybrid modelling technique based on machine learning and building dynamic simulation is presented for the prediction of indoor thermal comfort feedback from occupants in an office building in Le Bourget-du-Lac, Chambéry, France. The office was equipped with Internet of Things (IoT) environmental sensors. A calibrated building energy model was created for the building using optimisation tools. Thermal comfort was collected using a portable device. A machine learning (ML) model was trained using collected feedback, environmental data from IoT devices and synthetic datasets (virtual sensors) extracted from a physics-based model. A calibrated energy model was used in co-simulation with the predictive method to estimate comfort levels for the building. The results show the ability of the method to improve the prediction of occupant feedback when compared to traditional thermal comfort approaches of about 25%, the importance of information extracted from the physics-based model and the possibility of leveraging scenario evaluation capabilities of the dynamic simulation model for control purposes.