The energy performance and passive survivability of high thermal insulation buildings in future climate scenarios

Given that the passive performance simulation of buildings based on typical meteorological year data and specific design schemes makes it challenging to respond to climate change and refine design requirements on time,this article established a passive performance prediction model for future buildings considering multi-dimensional variables including climate change,building design,and operational characteristics.For high thermal insulation buildings under future climates,the mild climate zone is more sensitive than the others,cooling energy demand is more sensitive than heating demand,apartments are more sensitive than office buildings,and passive survivability is more sensitive than energy performance;for buildings of the same type located in the same climate zone,thermal design solutions determine the increase rate of cooling demand.The potential benefits of climate warming on heating demand reduction are almost zero,but the cooling demand increases significantly,with apartments and office buildings increasing up to 22.1% and 5.0%,respectively.Buildings generally overheat in the future,and the increase rate of the mild zone far exceeds other zones with duration and severity being 3004.8% and 877.7%for apartments,and 884.3% and 288.9%for office buildings,respectively.

Tradeoff between heating energy demand in winter and indoor overheating risk in summer constrained by building standards

Evidence indicates that improvement of thermal performance of building envelope has the potential for aggravating the indoor overheating risk in summer. On the other hand, evolving building standards continue to strengthen the requirements for thermal performance to achieve the energy-saving target. Therefore, this study quantifies the interaction effect between building standards-oriented building design, heating energy demand in winter, and indoor overheating risk in summer. Building databases with different energy efficiency levels are generated using a randomly generated method. Uncertain variables include not only 13 design parameters but also the running state of natural ventilation and external shading. The indoor overheating risk is assessed in terms of severity and duration. Finally, a multi-objective optimization model integrating metamodels and the non-dominated sorting genetic algorithm is proposed to balance heating energy demand in winter and indoor overheating risk in summer. Results indicate that building standards tend to aggravate overheating risk in summer: the duration and severity of high-performance buildings increased by 40.6% and 24.2% than that of conventional-performance buildings. However, window ventilation could offset the adverse effect, and mitigation of duration and severity can be up to 85.2% and 62.1% for high-performance buildings. Window ventilation can weaken the conflict between heating energy demand in winter and overheating risk in summer. As heating energy demand increased from 6.1 to 67.3 kWh/m^(2), the overheating risk changes little that the duration of overheating risk decreased from 17.5% to 15.6% and severity decreased from 8.7 ℃ to 8.3 ℃.

Review on occupancy detection and prediction in building simulation

Energy simulation results for buildings have significantly deviated from actual consumption because of the uncertainty and randomness of occupant behavior.Such differences are mainly caused by the inaccurate estimation of occupancy in buildings.Therefore,the error between reality and prediction could be largely reduced by improving the accuracy level of occupancy prediction.Although various studies on occupancy have been conducted,there are still many differences in the approaches to detection,prediction,and validation.Reports published within this domain are reviewed in this article to discover the advantages and limitations of previous studies,and gaps in the research are identified for future investigation.Six methods of monitoring and their combinations are analyzed to provide effective guidance in choosing and applying a method.The advantages of deterministic schedules,stochastic schedules,and machine-learning methods for occupancy prediction are summarized and discussed to improve prediction accuracy in future work.Moreover,three applications of occupancy models—improving building simulation software,facilitating building operation control,and managing building energy use—are examined.This review provides theoretical guidance for building design and makes contributions to building energy conservation and thermal comfort through the implementation of intelligent control strategies based on occupancy monitoring and prediction.