Data-driven Research on Characteristics of Operating Performance of Green Office Buildings
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Green building is crucial for promoting sustainable urbanization and mitigating environmental challenges. In recent decades, green buildings in China have witnessed a tremendous development. However, people still lack a sufficient understanding of their actual operating performance, resulting in deficient guidance for further green building design and operation. This thesis contributes to reveal the actual operating performance of green office buildings in China and provides various models for building energy and indoor environmental quality (IEQ) analysis, diagnosis and improvement.In this th...
Green building is crucial for promoting sustainable urbanization and mitigating environmental challenges. In recent decades, green buildings in China have witnessed a tremendous development. However, people still lack a sufficient understanding of their actual operating performance, resulting in deficient guidance for further green building design and operation. This thesis contributes to reveal the actual operating performance of green office buildings in China and provides various models for building energy and indoor environmental quality (IEQ) analysis, diagnosis and improvement.
In this thesis, the author carries out pioneering researches on data collection, characteristics cognition, performance diagnosis and optimization of green office buildings: (i) An intelligent IEQ monitoring and feedback system is developed for continuous data collection with a high spatial-temporal resolution. Based on that, 63 office buildings from different climate zones in China have been measured and a database with more than 100 million records is established, including building energy consumption, indoor air temperature, relative humidity, CO2, PM2.5, illumination and occupant satisfaction. (ii) Seasonal and regional distribution characteristics of IEQ in green office buildings are revealed, as well as its quantitative correlation with energy consumption and occupant satisfaction, respectively. Both positive and negative effects of energy saving on thermal comfort are found and the negative effect is more significant in buildings with natural ventilation. (iii) A variety of data-driven models with different requirements for data quality were proposed to diagnose and improve IEQ and building energy use, including IEQ diagnosis tool via Kano model, IEQ daily pattern clustering method, building energy diagnosis model based on regression, and HVAC system smart control method based on the building load prediction model. In summary, this thesis has important theoretical significance and practical value for promoting the healthy development of green buildings.
In this thesis, the author carries out pioneering researches on data collection, characteristics cognition, performance diagnosis and optimization of green office buildings: (i) An intelligent IEQ monitoring and feedback system is developed for continuous data collection with a high spatial-temporal resolution. Based on that, 63 office buildings from different climate zones in China have been measured and a database with more than 100 million records is established, including building energy consumption, indoor air temperature, relative humidity, CO2, PM2.5, illumination and occupant satisfaction. (ii) Seasonal and regional distribution characteristics of IEQ in green office buildings are revealed, as well as its quantitative correlation with energy consumption and occupant satisfaction, respectively. Both positive and negative effects of energy saving on thermal comfort are found and the negative effect is more significant in buildings with natural ventilation. (iii) A variety of data-driven models with different requirements for data quality were proposed to diagnose and improve IEQ and building energy use, including IEQ diagnosis tool via Kano model, IEQ daily pattern clustering method, building energy diagnosis model based on regression, and HVAC system smart control method based on the building load prediction model. In summary, this thesis has important theoretical significance and practical value for promoting the healthy development of green buildings.
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