Research On The Coordination Of Buildings,Occupants,and Digital Models In Building Energy Consumption Management

The operation and maintenance management(O&M Management)of buildings bears significance in saving operational costs,optimizing energy consumption,and reducing carbon emission of buildings,and the future of data-driven building energy consumption management lies in delicacy,smartness,and comprehensiveness.Currently,China has taken the step forward into the digitization of energy consumption management,yet problems exist in its practices: relevant data standards are coarse in granularity and incomplete;data from different sources are heterogeneous and difficult to fuse;occupant factors are deficiently accounted,and the coordination levels and interaction methods between buildings and occupants are relatively backwards.Analyses and applications of relevant data are consequently also restricted.These problems confined the improvement of China’s building energy consumption management levels,therefore making it vital to research the roles,characteristics,interaction mechanisms,and specific coordination techniques for the three major factors in building energy consumption management,namely buildings,occupants,and digital models.Addressing the aforementioned problems,and founded on BIM and Io T technologies,this study comprehensively employed emerging data technologies including pattern recognition and machine learning,and conducted systematic research on the coordination architecture and techniques,and supporting platforms of the three-factor coordination in building energy consumption management.First,through studying the content of building energy consumption management,and the roles and data characteristics of the three factors therein,this study proposed a coordination model and architecture for the three factors,and further identified the key technologies to be researched in successive studies.Then,regarding data requirements in energy consumption management process,this study designed data models for information categories including static,operational,environmental,energy consumption,and occupants,providing standardized description methods and storage mechanisms for relevant data,and on this basis researched the integration methods for heterogeneous energy consumption data.Next,addressing the detection and extraction of occupant information,this study designed an ensemblelearning-based detection scheme to relieve the problems of insufficient labelled data and the synchronous updating of learning models,while also designed an interaction method between buildings and occupants based on wall-surface touch control.Finally,addressing the major application scenario of data cleansing,this study established algorithms for anomaly detections and fixtures on the foundation of coordinated data,and further validated the effectiveness of the algorithms through comparison of prediction accuracies.Practical researches have suggested that the theories,techniques,methods,and the system established in this study suffice to achieve organic integration of the three factors of buildings,occupants,and digital models,thus blazed a technological path for realizing delicate,smart,and comprehensive management of the management of energy consumption of buildings,and therefore possess pragmatic theoretical significance,and broad prospects of application.