Predicting indoor environmental conditions using correlation models for behaviour change suggestions

Background: Behaviour changes by end-users have been seen as an effective action to tackle the global climate crisis and improve indoor and outdoor environmental quality, while energy and carbon savings and promoting health and well-being are notably observed. However, indoor environmental quality predictive modelling for participatory research has not been developed yet due to the lack of a user-friendly method. Purpose: We present a framework to predict indoor air temperature, air change for ventilation efficacy and indoor illuminance for daylight by correlating indoor and outdoor climates. Research Design: The method integrates indoor-outdoor climate correlation models, bioclimatic design, and occupant-centric control decision-making processes. The predictive modelling was developed from a series of pre-defined boundary conditions, and the case studies were demonstrated using an occupied multi-family apartment building in Switzerland. Result: The presented method uses real-time and forecasted outdoor weather to predict indoor environmental conditions and provides results for different building operation actions. Conclusions: Recommendations for practical applications are discussed according to Fogg’s behaviour model in developing the participatory research for the eco-feedback approach to applying the framework to behaviour interventions, considering increasing the ability, opportunities and motivation of end-users in predicting indoor environmental quality.