In response to the urgent need for energy-efficient retrofits in industrial buildings under global climate goals, this study presents a robust multi-objective optimization framework that integrates building performance simulation, surrogate modeling, evolutionary algorithms, and decision analysis. A representative old factory building in Wenzhou, China, is selected as the case study. DesignBuilder is used to simulate energy consumption, thermal comfort, and carbon emissions. To reduce computational costs, surrogate models based on Backpropagation Neural Networks (BPNN) and Support Vector Regression (SVR) are developed and compared in terms of predictive performance.The results show that BPNN demonstrates superior predictive accuracy compared to SVR, with higher R and lower RMSE values. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a set of Pareto-optimal solutions, and the entropy-weighted TOPSIS method is applied to identify the most balanced retrofit option. The optimized design results in a 10.06% reduction in thermal discomfort hours (Tdh), a 35.45% reduction in energy density index (EDI), and a 28.86% reduction in life-cycle carbon emissions (LCCO₂), respectively. Overall, the proposed framework proves to be highly applicable to the low-carbon renovation of existing industrial buildings, offering a practical and scalable decision-support approach for achieving a balance among energy efficiency, environmental sustainability, and indoor comfort.

As the field of zero energy building design and research continues to progress, the use of data analysis methods is on the rise. These methods are applied to create assessment criteria, compare performance, and aid in design decision making. Decision trees, as a data-driven approach, offer interpretability and predictability, assisting designers in summarizing their design experience and serving as a foundation for design references. However, the current application of decision tree methods in the zero energy house sector primarily focuses on HVAC systems, lacking a comprehensive exploration from an architectural design perspective. Therefore, this study presents an empirical method for building and applying models based on decision trees, using zero energy house cases in severely cold regions of China as samples. Through an analysis of the interactions among various passive design parameters and the use of EnergyPlus for performance simulations, a decision tree model is established. This model aids in determining the recommended combinations of passive design parameters that meet the criteria of low energy consumption. Moreover, feature weighting highlights the most influential passive design parameters on building energy consumption, including the length of the architectural gestalt plane, the roof shape, and the ground thermal resistance. This research provides valuable methods and guidance for the design and construction of zero energy houses in severely cold regions of China.

Solar Decathlon is a multi-disciplinary international competition that integrates energy-saving design strategies to design, build and operate zero-energy solar houses. This study focused on the 15 entries in the third Solar Decathlon China. It summarized their energy-saving design strategies into strategies of architectural design, equipment management, energy acquisition and intelligent regulation, and extracted a total of 22 key design elements. Based on the scoring results of the competition, this study analyzed the application of different design strategies with qualitative analysis; through quantitative analysis, 22 design elements were associated with the score, and the impact of different strategies on the score was comprehensively analyzed. As revealed in the data, design concept, functional structure and application type of renewable energy are significantly correlated with and have a great impact on the score; in contrast, building area and thermal buffer space are not significantly correlated with the score. On the basis of data analysis, this study provides a quantitative decision basis for the energy-saving design strategy of zero-energy buildings, and establishes an empirical model for the design of zero-energy solar buildings in Zhangbei County, Zhangjiakou City. This paper is helpful for the design practice and application of subsequent studies on ZEBs.

Building Information Modeling (BIM), as an auxiliary design platform, is increasingly adopted in construction projects. However, it is not widely applied in the collaborative design of zero energy buildings (ZEBs), due to the cross-discipline and complex features of ZEB projects and lack of research on the procedure and method of collaborative design in this field. This paper introduces a BIM-based collaborative design method for ZEBs. From the perspective of the technical requirements of ZEBs, the study elaborates the application of a BIM-based collaborative design method among specialists from different disciplines in passive design, renewable energy utilization and active design. The feasibility of this method is verified by the actual design and construction of the T&A House in Solar Decathlon China (SDC) competition. The research results show that the BIM-based collaborative design method can facilitate the completion of a construction project and achieve the expected goal of zero energy consumption in ZEBs.

With the intensification of the global energy crisis, the application of photovoltaic (PV) technology in the field of architecture has gradually become an important direction for sustainable building design. Especially in cold regions, how to choose the right photovoltaic technology and system type to improve energy efficiency is one of the current research hotspots. This paper investigates the performance of building integrated photovoltaics (BIPV) and building applied photovoltaics (BAPV) systems, as well as different photovoltaic (PV) technologies (including c-Si, CIS, and CdTe), for rural residences in cold regions. The study uses a representative rural residence in Xuzhou, China, as the benchmark model, with the installed capacity of the PV system set at 9.0 kW p. By evaluating key performance parameters, including annual power generation, yield factor, performance ratio, and system losses, the study provides a comparison of the BAPV and BIPV systems with different PV technologies. The results reveal that the c-Si-based BIPV system exhibits the lowest annual power generation and performance ratio, with the highest losses. In contrast, the CdTe-based BAPV system performs the best in terms of annual power generation and performance ratio, while also demonstrating the lowest losses. Overall, among these two systems, the BAPV system surpasses the BIPV system in overall efficiency; and at the technological level, CdTe technology outperforms both CIS and c-Si technologies.