Quantifying Thermal Resiliency and Passive Survivability During Prolonged Air Conditioning Outages in Extreme Hot Climate: Arizona Case Study Dataset

The purpose of the research is to evaluate thermal resiliency of building during prolonged AC outage scenario in hot climatic zone of Arizona using high-resolution field collected data. Hobo data loggers were placed at several locations (mid region, near ceiling, opposite wall, & near windows) of different rooms in a 2bedroom 2 bathroom apartment and a 1 bath 1 bedroom apartment to log indoor dry bulb temperature and relative humidity over 77-78 hours of AC outage scenario. Various thermal resiliency metrics were evaluated to better understand thermal response of the apartment. This dataset contains raw as well as processed data related to indoor thermal performance of buildings during controlled air- conditioning shut down and its recovery phase. Raw data includes indoor thermal indoor dry-bulb temperature (°F) and Relative Humidity (%) from 2 apartments in Arizona (One 2BHK apartment in Mesa, AZ and one 1BHK apartment in Chandler, AZ). The processed data should be mostly reproducible as the formulas are given and aren't hard coded except for the multivariate regression of sinusoidal equation developed to predict temperature of zone after transient time is exceeded during extended power outage duration. The dataset is designed to support research on thermal resiliency under extreme heat events, indoor heat exposure risks, recovery dynamics after cooling restoration, and passive survivability. The study uses a combined metrics like severity metrics, time-to threshold metrics, recovery rate, and sinusoidal equation to predict indoor temperature during prolonged AC outage. Following Indexes are calculated and normalized: Exposure(E): Thermal Exposure Index(TEI), and Intensity Index (II), Delay(Del): Delaying Index (DI), Dynamic(D):Thermal Time Constant(Tau), and Thermal Lag(Phi). Thermal Resiliency Index (TRI) is calculated using E,Del and D following formula: TRI= w1*E +w2*Del +w3*D (where W1=0.5,w2=0.3,w3=0.2 are weights assigned to each metric. More emphasis is given to metric E as it shows the exposure level which occupants will be exposed to. Recovery Resiliency Index (RRI) was calculated from time to cool back to setpoint temperature and cooling recovery rate. Thermal Resiliency Composite Index was evaluated by taking weighted average of TRI and RRI with higher weight on TRI than RRI. This ensures that both disruptive as well as recovery phase is captured while evaluating thermal resiliency. Also, a equation to to predict temperature of the apartment during power outage situation in extreme heat season is developed from zone level metrics. The equation is combination of exponential equation until transient temperature is reached and sinusoidal equation after transient time is reached. to predict indoor temperature during prolonged AC outage. Most of the research in thermal resiliency and passive survivability rely on building energy simulations and do not capture both disruptive as well as recovery phase. There is still lack of measurement and verification done in this endeavor. The dataset is designed to support research on thermal resiliency under extreme heat events, indoor heat exposure risks, recovery dynamics after cooling restoration, and passive survivability. Attached Readme file and the analysis files should provide more context and details.