Buildings account for a majority of the primary energy consumption of the human society, there-fore analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated over data collected from a multitude of large-scale public buildings across China.
This research was funded by the National Key R&D Program of China (grant No. 2017YFC0704200) and the National Natural Science Foundation of China (grant No. 51778336). This research was also supported by Tsinghua University – Glodon Joint Research Center for Building Information Model (RCBIM).
Accession Number: WOS:000624704400001
PubMed ID: 33671242
IDS Number: QQ7MB
2018.1-2021.12: Research on information-driven multi-scale performance simulation and analysis technologies for existing buildings