A performance-based method to detect and characterize heatwaves for building resilience analysis

Abstract

Buildings for the coming years need to achieve resilient designs for extreme climate events such as heatwaves. Building resilience analysis requires a method to detect and characterize the heatwave events that can affect the indoor environment or energy consumption. While there are several methods to detect heatwaves, their ability to predict the impact on the indoor environment was not studied yet. This work aims to compare three existing and popular models for detecting heatwaves, as well as to propose a novel method to detect those heatwaves that can have an important impact on the indoor environment. The selected models are those used in France, Australia, and Argentina. Heatwaves that occurred over a 15-year period (2006?2020) in Buenos Aires city were detected and analyzed. The impact of the detected heatwaves on the indoor overheating was quantified through the Indoor Overheating Degree (IOD) and related to the main heatwave characteristics through statistical analysis. A social single-family house was employed as the case-study and multi-year building simulations using EnergyPlus were carried out to calculate the IOD during each heatwave analyzed. The results showed that Ouzeau´s method was the most suitable to detect heatwaves for building applications Furthermore, suitable thresholds for grouping the heatwaves having moderate (IOD ≤0.5 °C), strong (0.5 °C < IOD <2.0 °C), and extreme impact (IOD ≥2.0 °C) on the indoor environment were attained. The proposed method showed to be useful for classifying heatwaves according to their impact on the indoor environment, which is of high interest for building resilience analysis.