Method of constructing stochastic near-extreme daily weather data for efficient calculation of probabilistic load in air-conditioning system design

• Published in: Building and environment 2022-08, Vol.221, p.109278, Article 109278
• Main Authors: Wu, Xia, Niu, Jide, Tian, Zhe, Hou, Xinyang, Zhou, Ruoyu
• Format: Article
• Language: English
• Subjects: Air-conditioning design load; Near-extreme weather data; Probabilistic method; Stochastic simulation; Uncertainty quantification
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2022.109278

To solve the oversizing problem of the air-conditioning systems caused by the deterministic method of calculating design load, the probabilistic method has been proposed, in which the design load is selected based on the load probability distribution. In the probabilistic method, obtaining the probabilistic load based on the annual weather scenarios is time-consuming. Thus, it seems more efficient to determine the design load by obtaining the probabilistic near-extreme load directly through stochastic simulation. Therefore, a refined stochastic near-extreme daily weather (SNDW) model, in which the coupling of weather parameters was considered, was proposed to quantify the uncertainty of weather parameters in the stochastic simulation of near-extreme load. Taking Tianjin in China as an example, the SNDW model was established, and the probabilistic design loads of three types of buildings were calculated on this basis. The results showed that compared to the benchmark method, the application of the SNDW model could ensure about 1.5% of the relative error of the design load selected, and the average simulation time was reduced to 1/23 of the benchmark. Finally, the adaptability of the SNDW model to different climatic zones was verified. •A refined stochastic near-extreme daily weather (SNDW) model is proposed.•The coupling of weather parameters is taken into account.•The effect of the SNDW model is validated.•The adaptability of the SNDW model to different climatic zones is verified.