Infrastructure Engineering - Theses
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ItemImproving building energy efficiency: biomimetic adaptive façade and computational data-driven approach( 2020)The urbanisation and population growth are resulting in a significant increase in energy consumption in buildings, leading to a substantial increase in greenhouse gas (GHG) emissions. During the operation of buildings, a massive amount of GHG emissions are released due to the process of building heating, cooling, and lighting, which accounts for the most significant proportion in building energy consumption. Therefore, energy-efficiency design and operation will play an essential role in reducing GHG emissions in buildings. Facade systems are one of the most critical aspects regarding the efficient management of heating, cooling, and lighting energy in buildings. A facade system is a barrier and exchanger (simultaneously) for temperature, light, and air between the building indoor environment and the outside environment. Therefore, the proper design and operation of the facade can effectively save substantial energy. For decades, engineers and researchers from all over the world have been in search for the intelligent design and operation of the facade systems to improve energy efficiency and sustainability in buildings, and to not compromise a pleasant indoor environment for building occupants. Subsequently, they have found that many natural systems have developed a highly efficient biological structure to adapt to dynamic and extreme environments over millions of years. These natural systems now have become great inspirations for the research community in the quest for building energy efficiency solutions, and the biomimetic adaptive facade (BAF) system is one of those remarkable examples of adopting bioinspiration in buildings. The BAF system is considered as a potential solution to improve the performance of conventional facade systems. The BAF system has an ability to adapt its functions, features, or behaviour for dynamically varying climatic conditions, providing buildings with the operational flexibility to act in response to different climate scenarios. Nonetheless, the practical application of a BAF in buildings remains limited due to the absence of a comprehensive design platform that can facilitate the widespread adoption of BAF systems. Most studies on BAFs remain at a conceptual stage of development, and an effective platform that can effectively assist the design and operation of BAF is still lacking. This thesis proposes and develops a methodology for enhancing building energy efficiency using the design of BAF systems, and thereby supports the transition to next-generation facades. Specifically, the objective of this thesis is to develop, test, and evaluate a computational data-driven optimisation approach in assisting the BAF design. The thesis presents a multidisciplinary approach that combines building energy modelling, metaheuristic optimisation, and data-driven methods. The goal of the proposed approach is to minimise the total energy consumption in buildings, including heating, cooling, and lighting energy, but still maintain the indoor environmental quality in terms of thermal and visual performance. A comprehensive analysis of the proposed computational data-driven optimisation approach is provided in the thesis. In summary, this study has proposed a computational data-driven approach based on building energy simulations, optimisation processes, and machine learning algorithms. The proposed approach is used to assist the design and operation of BAFs for building energy efficiency and analyse the interactions between energy-saving and indoor environmental quality. These significant findings demonstrate the potential of BAFs to enhance the energy efficiency of buildings, and the developed platform can be used as an effective tool to support BAFs in both design and product development.