Development of an Integrated Performance Design Platform for Residential Buildings Based on Climate Adaptability

Intelligent Power Distribution Restoration Based on a Multi-Objective Bacterial Foraging Optimization Algorithm

The importance of power in society is indisputable. Virtually all economic activities depend on electricity. The electric power systems are complex, and move studies in different areas are motivated to make them more efficient and solve their operational problems. The smart grids emerged from this approach and aimed to improve the current systems and integrate electric power using alternative and renewable sources. Restoration techniques of these networks are being developed to reduce the impacts caused by the usual power supply interruptions due to failures in the distribution networks. This paper presents the development and evaluation of the performance of a multi-objective version of the Bacterial Foraging Optimization Algorithm for finding the minor handling switches that maximize the number of buses served, keeping the configuration radial system and within the limits of current in the conductors and bus voltage. An electrical system model was created, and routines were implemented for the network verification, which was used as a function of the Multi-Objective Bacterial Foraging Optimization Hybrid Algorithm. The proposed method has been applied in two distribution systems with 70 buses and 201 buses, respectively, and the algorithm’s effectiveness to solve the restoration problem is discussed.

Research on Optimization of Climate Responsive Indoor Space Design in Residential Buildings

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.