Our paper “An Innovative Modelling Approach Based on Building Physics and Machine Learning for the Prediction of Indoor Thermal Comfort in an Office Building”, written by Giovanni Tardioli (IES), Ricardo Filho (IES), Pierre Bernaud (CEA) and Dimitrios Ntimos (IES) was published as an open access article in the special 10th anniversary edition of Buildings which is part of the MDPI Open Access platform.
The paper is about the estimation of indoor thermal comfort and the associated occupant feedback in office buildings in order to provide satisfactory and safe working environments, enhance the productivity of personnel, and to reduce complaints via the deployment of the iBECOME vBMS.
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. 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%.
The paper is available to download here https://www.mdpi.com/2075-5309/12/4/475
A shorter version of the paper was submitted and presented in Sustainable Places 2021 conference.
View the presentation here https://www.youtube.com/watch?v=6OFh7mSm2X4&t=4057s