The traditional energy-saving evaluation method for industrial buildings is intended for all industrial buildings; however, substation buildings belong to a special category of industrial buildings, and their energy consumption characteristics are different from those of general industrial buildings. Consequently, it is necessary to establish an energy-saving evaluation system for substation buildings according to the characteristics of their energy consumption. In view of the issue that the energy consumption characteristics of substation buildings are different from those of other industrial buildings, an analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) are used to establish a more comprehensive energy saving evaluation model that is more applicable to substation buildings. This paper determines 19 quantitative indicators and 13 qualitative indicators through the screening of relevant standards and norms, as well as the literature, and then determines the weight of each indicator by using AHP before finally establishing a secondary evaluation model based on FCE. In this paper, a substation in Shandong, China was selected as a case study to verify the proposed evaluation model, scoring 80.4 points, which falls within the “Good” grade. This method is of great significance for the future establishment of energy-saving evaluation system for substation buildings.

Optimizing building performance and economic benefits through feedback in building design is a hot topic in current academic research. However, few studies on prefabricated buildings have been undertaken in this field. Meanwhile, the methodology used for achieving optimized solutions is still poor. In this paper, genetic algorithms and correlation analysis are employed and two parametric design methods—i.e., the floor plan generation method and the component selection method—are proposed for the modularity of the prefabricated buildings. Taking a typical high-rise building in Tianjin as an example, correlation analyses are performed on the basis of the two proposed methods to enhance the depth of the optimized finding approach. The outcome of this research demonstrates the feasibility of the proposed numerical approach, which can produce the optimized floor plan and construction set under the local conditions. This also reveals that the shape coefficient and window-to-wall ratio are strongly correlated with the energy performance of a building, which can help architects to pursue optimized design solutions in the schematic design process.

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.