Monitoring and analytics to measure heat resilience of buildings and support retrofitting by passive cooling

Abstract

Designing buildings to prevent indoor overheating requires the definition of accurate procedures to measure the passive survivability of buildings and support retrofitting. This research proposes innovative diagnostic methods to audit the heat resilience of buildings using long-term monitoring data of temperature and CO2 concentrations. The aim is to identify optimal passive cooling alternatives to retrofit the built environment through a speedy and less-disruptive assessment of the actual building performance. The approach focuses on three steps: (1) characterisation of the overheating situation of the indoor environment by a novel seasonal building overheating index (SBOI) ranging from 0 to 100%; (2) diagnosis of the indoor environment through a heat balance map that divides building performance into four thermal stages related to the positive or negative influence of total heat flux, and the ventilation and infiltration load; (3) and calculation of air change rates associated with ventilation and infiltration per thermal stage using the CO2-based decay method. The diagnostic analytics were developed in Python and tested on three homes. The results demonstrate how the proposed approach can efficiently characterise the overheating situation of buildings, with Home 2 showing the most vulnerable scenario (SBOI>35%). Moreover, the indicators identified the best available passive cooling opportunities concerning the reduction of solar and heat gains for Home 2, and the increase of ventilative cooling for Home 1. The research highlights the role of diagnostic analytics using real monitoring data to audit seasonal building performance beyond standard tests and simulations. The source code can be found at https://github.com/lizanafj/analytics-to-assess-the-heat-resilience-of-buildings.