With the advance of the internet of things and building management system(BMS)in modern buildings,there is an opportunity of using the data to extend the use of building energy modeling(BEM)beyond the design phase.Pot...With the advance of the internet of things and building management system(BMS)in modern buildings,there is an opportunity of using the data to extend the use of building energy modeling(BEM)beyond the design phase.Potential applications include retrofit analysis,measurement and verification,and operations and controls.However,while BMS is collecting a vast amount of operation data,different suppliers and sensor installers typically apply their own customized or even random non-uniform rules to define the metadata,i.e.,the point tags.This results in a need to interpret and manually map any BMS data before using it for energy analysis.The mapping process is labor-intensive,error-prone,and requires comprehensive prior knowledge.Additionally,BMS metadata typically has considerable variety and limited context information,limiting the applicability of existing interpreting methods.In this paper,we proposed a text mining framework to facilitate interpreting and mapping BMS points to EnergyPlus variables.The framework is based on unsupervised density-based clustering(DBSCAN)and a novel fuzzy string matching algorithm“X-gram”.Therefore,it is generalizable among different buildings and naming conventions.We compare the proposed framework against commonly used baselines that include morphological analysis and widely used text mining techniques.Using two building cases from Singapore and two from the United States,we demonstrated that the framework outperformed baseline methods by 25.5%,with the measurement extraction F-measure of 87.2%and an average mapping accuracy of 91.4%.展开更多
文摘With the advance of the internet of things and building management system(BMS)in modern buildings,there is an opportunity of using the data to extend the use of building energy modeling(BEM)beyond the design phase.Potential applications include retrofit analysis,measurement and verification,and operations and controls.However,while BMS is collecting a vast amount of operation data,different suppliers and sensor installers typically apply their own customized or even random non-uniform rules to define the metadata,i.e.,the point tags.This results in a need to interpret and manually map any BMS data before using it for energy analysis.The mapping process is labor-intensive,error-prone,and requires comprehensive prior knowledge.Additionally,BMS metadata typically has considerable variety and limited context information,limiting the applicability of existing interpreting methods.In this paper,we proposed a text mining framework to facilitate interpreting and mapping BMS points to EnergyPlus variables.The framework is based on unsupervised density-based clustering(DBSCAN)and a novel fuzzy string matching algorithm“X-gram”.Therefore,it is generalizable among different buildings and naming conventions.We compare the proposed framework against commonly used baselines that include morphological analysis and widely used text mining techniques.Using two building cases from Singapore and two from the United States,we demonstrated that the framework outperformed baseline methods by 25.5%,with the measurement extraction F-measure of 87.2%and an average mapping accuracy of 91.4%.
