Monitoring and analytics to measure heat resilience of buildings and support retrofitting by passive cooling
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Materia
Data science Overheating Data analytics Heat resilience Building performance analysis Passive cooling
Fecha
2022-07-19Referencia bibliográfica
Elisa López-García... [et al.]. Monitoring and analytics to measure heat resilience of buildings and support retrofitting by passive cooling, Journal of Building Engineering, Volume 57, 2022, 104985, ISSN 2352-7102, [https://doi.org/10.1016/j.jobe.2022.104985]
Patrocinador
ERDF for the Andalusian region US -15547; Andalusian Government US.20-06; European Commission 101023241; Andalusian Government (Junta de Andalucia-Consejeria de Economia, Innovacion y Ciencia) POST- DOC_21-00575; Spanish GovernmentResumen
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.