A graph mining-based methodology for discovering and visualizing high-level knowledge for building energy management

Building operations have evolved to be not only energy-intensive, but also information-intensive. Advanced data-driven methodologies are urgently needed to facilitate the tasks in building energy management. Currently, there are two main bottlenecks in analyzing building operational data. Firstly, few methodologies are available to represent and analyze data with complicated structures. Conventional data analytics are capable of analyzing information stored in a single two-dimensional data table, while lacking the ability to handle multi-relational databases. Secondly, it is still challenging to visualize the analysis results in a generic and flexible fashion, making it ineffective for knowledge interpretations and applications. As a promising solution, graphs can integrate and represent various types of information, providing promising approaches for the knowledge discovery from massive building operational data. This study proposes a novel graph-based methodology to analyze building operational data. The methodology consists of various stages and provides solutions for data exploration, graph generations, knowledge discovery and post-mining. It has been applied to analyze the actual building operational data of a public building in Hong Kong. The research results validate the potential of the graph-based methodology in characterizing high-level building operation patterns and atypical operations.