This book facilitates more comprehensive understandings of operating performance of green buildings and provides technical supports for high-quality developments in building industry. In recent years, the development of green building is facing a transformation from “quantity” to “quality.” The acquisition of actual performance data (including energy consumption, IEQ, and occupant satisfaction) and its in-depth quantitative research are of great significance to comprehensively improve the quality of green buildings. However, there was a lack of high-quality, long-term and large-scale operating performance data of green buildings, which resulted in shallow and incomplete cognitions of performance characteristics and failures to guide building operation.In this thesis, the author carries out pioneering researches on data collection, characteristics cognition, performance diagnosis and optimization: (i) An intelligent IEQ monitoring and feedback system is developed for continuous data collection with a high spatial-temporal resolution. Based on that, 63 buildings from different climate zones in China have been measured and a database with more than100 million records is established, including 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. 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 series of data-driven analysis and diagnosis models are proposed, such as the IEQ diagnosis method based on multi-dimensional dynamic field, the regression model between energy use and outdoor weather, the coupling approach of data mining models and physical functions for building performance diagnosis and optimization.

This book facilitates more comprehensive understandings of operating performance of green buildings and provides technical supports for high-quality developments in building industry. In recent years, the development of green building is facing a transformation from “quantity” to “quality.” The acquisition of actual performance data (including energy consumption, IEQ, and occupant satisfaction) and its in-depth quantitative research are of great significance to comprehensively improve the quality of green buildings. However, there was a lack of high-quality, long-term and large-scale operating performance data of green buildings, which resulted in shallow and incomplete cognitions of performance characteristics and failures to guide building operation.In this thesis, the author carries out pioneering researches on data collection, characteristics cognition, performance diagnosis and optimization: (i) An intelligent IEQ monitoring and feedback system is developed for continuous data collection with a high spatial-temporal resolution. Based on that, 63 buildings from different climate zones in China have been measured and a database with more than100 million records is established, including 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. 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 series of data-driven analysis and diagnosis models are proposed, such as the IEQ diagnosis method based on multi-dimensional dynamic field, the regression model between energy use and outdoor weather, the coupling approach of data mining models and physical functions for building performance diagnosis and optimization.