Abstract
A large amount of building energy consumption data has been accumulated nowadays. Such data can help to find problems in building energy efficiency and put forward suggestions for imporvements. However, the present situation is that a big amount of data has been mothballed and its value has not been fully exploited. In this paper, clustering method is used to analyse the energy consumption data of 784 public buildings in Guangzhou. The method combines K-means algorithm and Euclidean distance for similarity measure, aiming to classify the time-series data of energy consumption. The analysis results identify well the energy consumption trends of these buildings. Three clusters of buildings with incresing energy consumption trends are taken as key target for energy conservation and four clusters of buildings with downward energy consumption trends are treated as example buildings for experience learning. A further analysis of the clusters finds the characteristics of each group, this helps understand better the patterns of building energy consumption. The clustering analysis facilitates more effective diagnosis in energy efficiency and also supports policy making regarding building energy conservation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Earth and Environmental Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
