Discovering Complex Knowledge in Massive Building Operational Data Using Graph Mining for Building Energy Management

Cheng Fan, Mengjie Song, Fu Xiao, Xue Xue
2019 Energy Procedia  
District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand
more » ... door temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. Abstract Discovering useful knowledge from massive building operational data is considered as a promising way to improve building operational performance. Conventional data analytics can only handle data stored in a single two-dimensional data table, while lacking the ability to represent and analyze data in complex formats (e.g., multi-relational databases). Graphs are capable of integrating and representing various types of information, such as spatial information and affiliations. The knowledge discovery based on graph data can therefore be very helpful for revealing complex relationships in building operations. This study proposes a novel methodology for analyzing massive building operational data using graph-mining techniques. Two problems are specifically addressed, i.e., graph generation based on building operational data and knowledge discovery from graph data. The methodology has been applied to analyze the building operational data retrieved from a real building in Hong Kong. The research results show that the knowledge obtained is valuable to characterize complex building operation patterns and identify atypical operations. Abstract Discovering useful knowledge from massive building operational data is considered as a promising way to improve building operational performance. Conventional data analytics can only handle data stored in a single two-dimensional data table, while lacking the ability to represent and analyze data in complex formats (e.g., multi-relational databases). Graphs are capable of integrating and representing various types of information, such as spatial information and affiliations. The knowledge discovery based on graph data can therefore be very helpful for revealing complex relationships in building operations. This study proposes a novel methodology for analyzing massive building operational data using graph-mining techniques. Two problems are specifically addressed, i.e., graph generation based on building operational data and knowledge discovery from graph data. The methodology has been applied to analyze the building operational data retrieved from a real building in Hong Kong. The research results show that the knowledge obtained is valuable to characterize complex building operation patterns and identify atypical operations.
doi:10.1016/j.egypro.2019.01.378 fatcat:a2e4e7eirbddjmu726qzltaxym