Adaptation to extreme weather events using pre-conditioning: a model-based testing of novel resilience algorithms on a residential case study

Abstract

Climate change increases the frequency and intensity of extreme weather events that can be a prominent cause of power outages in North America. These events may cause buildings to experience outages for hours to days, endangering occupant well-being. Although typical adaptive strategies can offer assistance, they often demand substantial initial investments. Thus, due to the need for low-cost solutions, this paper evaluates the efficacy of the proposed pre-heating/cooling algorithm using smart thermostats. The ongoing research employs automated energy modelling through Python scripting to streamline the energy model upgrade process and the EnergyPlus Energy Management System (EMS) algorithm to incorporate pre-conditioning features during grid outages. The results indicated an average 18% improvement in peak intensity and 9% in overall performance during extreme events. Also, it offers the potential for future studies to employ this methodology in assessing the effects of other low-cost strategies for adapting to grid disruptions.