Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Building Environment and Energy Engineering | en_US |
| dc.contributor.advisor | Wang, Shengwei (BEEE) | en_US |
| dc.contributor.advisor | Li, Hangxin (BEEE) | en_US |
| dc.creator | Zhao, Zeming | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13414 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Coordinated optimal design of zero/low energy buildings in high-density cities considering their interaction with local microclimate | en_US |
| dcterms.abstract | Load reduction is a fundamental means for achieving the goal of zero/low-energy buildings and for accomplishing carbon-neutrality. Zero/low energy buildings with low energy demand and high utilization of renewable energy are therefore recognized as effective means to facilitate carbon neutrality, and are receiving increasing attention from government, society and professionals. As the world undergoing an intense process of urbanization, the development of high-density cities becomes rapid. In high-density areas, buildings can modify the surrounding microclimate and are recognized as one of the main contributors to the urban local microclimate. Meanwhile, the microclimate also has a considerable impact on the building energy performance. However, there is still lack of an effective design optimization method to identify global optimal solutions enhancing both building energy performance and pedestrian thermal comfort while considering the interaction between buildings and the local microclimate. The mutual impacts between them are ignored in current optimal design practices for zero/low energy buildings due to a lack of comprehensive understanding. In addition, the accurate prediction of the local microclimate surrounding the building with low computing cost is currently absent, which is the foundation for effective optimization. | en_US |
| dcterms.abstract | This study therefore aims to develop an effective and comprehensive optimal design method based on multi-objective optimization for zero/low energy buildings and local microclimate, considering their interactions in high-density cities. Machine learning-based surrogate models are also developed for fast evaluation of the local microclimate. | en_US |
| dcterms.abstract | The most influential design parameters of high-rise and low-rise buildings in different climate zones are identified by sensitivity analysis, and the impacts of climate and building height are studied and compared. A total of thirty-five design parameters under five categories are considered. Five Chinese climate zones covering three typical climates worldwide are researched. The key design parameters affecting winter thermal discomfort in climate zones typically without heating provision are also identified. The impact of thermal bridge on building energy performance is further investigated. Remarkable finding is that overhangs are among the most important elements for high-rise buildings in all climate zones concerned, while skylights are among the most influential elements for low-rise buildings concerning building load. | en_US |
| dcterms.abstract | A comprehensive and systematic analysis is conducted to investigate the mutual impacts between new individual building design and the local microclimate, and to identify the major influential building parameters on both local microclimate and building energy performance in subtropical urban area. A large number of high-resolution microclimate and building simulations based on advanced GIS spatial analysis technique are performed under different building designs for the mutual impact assessment. A global sensitivity analysis is conducted to identify the major influential building parameters. The results show that different building designs lead to significant variation of local wind velocity (i.e., -0.95~+4.51 m/s) and air temperature (i.e., -0.60~+1.17 K), while the local microclimate results in a change in the building energy consumption from -41.75kJ/m2 to 291.54kJ/m2. | en_US |
| dcterms.abstract | Machine learning-based surrogate models are developed to predict the impacts of local microclimate (i.e., local air temperature and wind velocity) due to the addition of a new individual building in high-density urban area. Two complementary machine learning-based surrogate models are identified and recommended for their high accuracy and high efficiency, including an SVR-based local air temperature model and a LightGBM-based local wind velocity model. They are identified by evaluating and comparing eight alternative machine learning models, four for each model development. 200 sets of CFD simulation data corresponding to different building designs are used for the model training and validation. The results show that the developed surrogate models can dramatically reduce computation time (from over 5 hours to less than a second for a single prediction) while keeping the same order of accuracy of CFD simulations for local microclimate prediction of individual buildings. It therefore facilitates the fast, comprehensive and accurate prediction of the impacts on the local microclimate at the early design stage of new construction and renovation of individual buildings, for designers to deliver preferred local microclimate and/or avoid unacceptable microclimate changes. | en_US |
| dcterms.abstract | A coordinated design optimization method is proposed, allowing the design optimization of a zero/low energy building and its microclimate to be achieved within practically affordable time by adopting an effective quantification method. Local microclimate surrogate models and automated building simulation are integrated with the optimizer to enhance the optimization efficiency and generalizability. The essential design variables can, therefore, be optimized comprehensively with affordable computation efforts using multi-objective optimization. The global optimal solutions (i.e., Pareto front) identified by NSGA-II are further evaluated using the entropy-TOPSIS method to determine the best solution. The proposed method is tested and validated by implementing it in a development case of integrated building in Hong Kong. The results show that, when using the coordinated optimal design method, the total building energy consumption can be saved up to 63.6% and the pedestrian thermal discomfort degree can be reduced up to 1.9 K. The computation time of a design optimization is reduced by 99.98% (i.e., from 42684.44 to 8.89 hours) compared with that using conventional simulation methods. | en_US |
| dcterms.extent | xxiii, 213 pages : color illustrations, maps | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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