@article{35248,
  keywords = {Climate change, Optimization, Pre-cooling, Heat wave, District-scale, Thermal resilience},
  author = {Zhaoyun Zeng and Wanni Zhang and Kaiyu Sun and Max Wei and Tianzhen Hong},
  title = {Investigation of pre-cooling as a recommended measure to improve residential buildings’ thermal resilience during heat waves},
  abstract = {<p><span>More intense heat waves are expected to occur more frequently in the twenty-first century. During severe heat waves, cooling capacity shortfall and overheating are likely to occur in residential buildings, and this will adversely affect occupant's thermal comfort and productivity. We propose a strategy of pre-cooling the house during off-peak hours to mitigate overheating during heat waves. Simulation results of a prototype single-family house show that adopting the rule-based control (RBC) of pre-cooling thermostat&nbsp;</span><a class="topic-link" title="Learn more about setpoint from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/engineering/setpoints">setpoint</a><span>&nbsp;schedule is effective in reducing thermal discomfort, and that the efficacy of pre-cooling depends upon several building characteristics. An optimized control (OC) of the thermostat setpoint schedule was developed based on the simulation of a prototype building. A simplified yet improved RBC (IRBC) pre-cooling schedule was then extracted from the OC schedule for practical implementation at a larger scale. The effects of the RBC schedule and IRBC schedule were evaluated in the King District of Fresno, which contains 814 residential buildings. Results show that both thermostat setpoint schedules can reduce overheating effectively and that IRBC is slightly better than RBC for most buildings. The findings support the California government's recommendation on pre-cooling to mitigate overheating, which can be further improved with an optimized thermostat setpoint schedule broadcast to residents through early alert messages before a heat wave.</span></p>},
  year = {2022},
  journal = {Building and Environment},
  volume = {210},
  pages = {108694},
  month = {02/2022},
  issn = {03601323},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0360132321010842},
  doi = {10.1016/j.buildenv.2021.108694},
  language = {eng},
}