Abstract
This paper proposed an optimization method to minimize the building energy consumption and visual discomfort for a passive building in Shanghai, China. A total of 35 design parameters relating to building form, envelope properties, thermostat settings, and green roof configurations were considered. First, the Latin hypercube sampling method (LHSM) was used to generate a set of design samples, and the energy consumption and visual discomfort of the samples were obtained through computer simulation and calculation. Second, four machine learning prediction models, including stepwise linear regression (SLR), back-propagation neural networks (BPNN), support vector machine (SVM), and random forest (RF) models, were developed. It was found that the BPNN model performed the best, with average absolute relative errors of 3.27% and 1.25% for energy consumption and visual comfort, respectively. Third, six optimization algorithms were selected to couple with the BPNN models to find the optimal design solutions. The multi-objective ant lion optimization (MOALO) algorithm was found to be the best algorithm. Finally, optimization with different groups of design variables was conducted by using the MOALO algorithm with the associated outcomes being analyzed. Compared with the reference building, the optimal solutions helped reduce energy consumption up to 34.8% and improved visual discomfort up to 100%.
Highlights
Many studies on passive buildings have focused on the optimization of the building envelope design parameters [1,2,3,4,5,6]
It can be noticed that the recommended value ranges for the overhang length, absorptance of solar radiation, concrete thickness, and insulation thickness for different parts of the building differ, which means that the building envelopes can be customized to achieve optimal performance
A passive building with a green roof located in Shanghai in the hot summer and cold winter region of China, was optimized by taking the building energy consumption and visual discomfort as the objectives
Summary
Many studies on passive buildings have focused on the optimization of the building envelope design parameters [1,2,3,4,5,6]. Asadi et al [8] conducted retrofit optimization for a residential building considering external wall/roof insulation, window type, and solar collector installation to minimize retrofit cost, maximize energy saving, and improve thermal comfort. There have been a number of studies on the performance of green roofs, mainly focusing on the physical properties of the plants and soil He et al [10] developed a heat and moisture transfer model to evaluate the insulation and temperature regulation effect of a green roof. The design variables mainly include window properties (e.g., window type and window-to-wallratio), wall/roof properties (e.g., concrete thickness and insulation thickness), building dimensions (e.g., number of floors and shape factor), and sun room properties (e.g., sun room depth). The design variables mainly focus on the properties of the plants, leaves, and soil (e.g., plant height, leaf area index, leaf reflectivity, and soil reflectivity), and the thermal conductivity of the substrate
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