Air-based BIPV/T is of significant research interest in reducing energy load and improving indoor comfort. As many factors related to meteorology, geometry and operation contribute to the thermal performance of BIPV/T, especially for one kind of hybrid air-based BIPV/T (HAB-BIPV/T), quantifying the effects of such uncertain parties is essential. In this paper, a numerical analysis was conducted regarding 13 parameters of one HAB-BIPV/T prototype. For each quantity of interest, the kernel density estimate was regarded as an approximation to the probability density function to assess uncertainty propagation. A sequential sensitivity analysis was used to quickly screen (by Morris) and exactly quantify (by Sobol’) the effects of significant variables. The surrogate model based on a back propagation neural network was employed to dramatically reduce the computational cost of Monte Carlo analysis. The results show that the uncertain inputs discussed can induce considerable fluctuations in the three quantities of interest. The most significant parameters on AUI include air inlet height, cavity thickness, air inlet velocity and number of air inlets. The outcomes of this study provide insights into the correlation between various factors and the thermal efficiency of the HAB-BIPV/T as a reference for similar design works.

A great abundance of rural houses lacking design guidance exists in the cold regions of China, often accompanied by huge energy loss. Particularly, a courtyard-style dwelling (CSD) has more complex and diverse building elements than a common house, rendering the design optimization extremely costly. Sensitivity analysis (SA) can screen the significant parameters of energy consumption for prediction and optimization. In this paper, (1) the design variables related to CSDs and their data details were extracted; (2) a ranking of parameters sensitive to energy demand was formulated; (3) an energy prediction model was trained and (4) dual-objective optimization was carried out. Using the survey data from 150 units in nine villages, 25 control variables were extracted for sequential global sensitivity analysis (GSA). Thus, the ranking of sensitivity parameters was formulated with the two-stage-and-three-sort GSA method. Furthermore, an energy prediction model was then trained with Gaussian Process Regression (GPR) and compared with the other four high-precision models. Based on the obtained prediction model, optimization was then carried out on energy and economic concerns. Consequently, a GSA-based workflow for CSD optimization was proposed to help architectural designers figure out the most efficient energy-saving parameter strategy.

This study is the first to conduct a global sensitivity analysis to identify the crucial variables that have an impact on the energy consumption of substations. The peak cooling and heating energy consumption, as well as the annual cooling and heating energy consumption of a substation in Shandong, are all simulated basing the Monte Carlo method. The simulation outputs are discussed by uncertainty analysis to obtain more accurate energy consumption thresholds. Subsequently, the treed Gaussian process and the standardized rank regression coefficient are used to perform a global sensitivity analysis of the simulation outputs. The results of the two global sensitivity analyses are practically the same, demonstrating that robustness can be ensured by simultaneously using two methods based on different theories. In addition, this study provides an efficient method for the energy-saving retrofitting of the existing substation and the energy-saving design of green substations in the future.