The existing building stock faces the challenge of low energy efficiency and requires renovation and upgrading to meet society′s goals of carbon reduction and sustainable development. This study presents an optimization framework utilizing genetic algorithms to develop robust retrofit plans that balance the need for improved energy efficiency, cost-effectiveness considerations for householders, and uncertainties regarding climate conditions. A case study of an aged residential building in a hot and humid region of China is used to demonstrate the proposed method. The optimization results show a potential energy demand reduction of 81.5%. However, due to the relatively long time required to realize economic benefits from high investments, short-term optimization tends to favor solutions with high energy demand and low primary costs. To effectively reduce carbon emissions, it is recommended to consider the long-term economic benefits of retrofits and prioritize solutions with high energy efficiency. However, it is important to acknowledge that the expensive nature of retrofit investments may pose barriers to residents. Society should provide adequate support and guidance to facilitate residential renovation efforts.
This paper first analyzes the climate characteristics of five typical cities in China, including Harbin, Beijing, Shanghai, Shenzhen and Kunming. Then, based on Grasshopper, Ladybug and Honeybee analysis software, according to the indoor layout of typical residential buildings, this research extracts design parameters such as the depth and width of different rooms and their window-to-wall ratios etc., to establish a climate responsive optimization design process with indoor lighting environment comfort, with heating and cooling demand as the objective functions. Meanwhile, based on Monte Carlo simulation data, ANN (Artificial Neural Network) is used to establish a prediction model to analyze the sensitivity of interior design parameters under different typical cities’ climatic conditions. The study results show that the recommended values for the total width and total depth of indoor units under the climatic conditions of each city are both approximately 14.97 m and 7.88 m. Among them, under the climatic conditions of Harbin and Shenzhen, the design parameters of residential interiors can take the recommended value of UDI optimal or nZEB optimal. While the recommended values of window-to-wall ratios for the north bedroom, master bedroom and living room in Shanghai residential interiors are 0.26, 0.32 and 0.33, respectively. The recommended value of the window-to-wall ratio of the master bedroom in Kunming residences is 0.36, and that of the remaining rooms is between 0.15 and 0.18. The recommended values of window-to-wall ratios for the master bedroom and living room in Beijing residences are 0.41 and 0.59, respectively, and that for the remaining rooms are 0.15. The multi-objective optimization process based on parametric performance simulation used in the study can effectively assist architects in making energy-saving design decisions in the preliminary stage, allowing architects to have a case to follow in the actual design operation process.
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