Advances and challenges in building engineering and data mining applications for energy-efficient communities. (August 2016)

• Record Type: Journal Article
• Authors: Yu, Zhun (Jerry); Haghighat, Fariborz; Fung, Benjamin C.M.
• Is Part Of: Sustainable cities and society. Volume 25(2016)
• Page Start: 33
• Page End: 38
• Publication Date: 2016-08
• Subjects: Data Mining -- Building Energy Use -- Occupant Behavior -- Big Data -- Review; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2015.12.001
• Languages: English
• ISSNs: 2210-6707

Abstract

Abstract: The rapidly growing and gigantic body of stored data in the building field, coupled with the need for data analysis, has generated an urgent need for powerful tools that can extract hidden but useful knowledge of building performance improvement from large data sets. As an emerging subfield of computer science, data mining technologies suit this need well and have been proposed for relevant knowledge discovery in the past several years. Aimed to highlight recent advances, this paper provides an overview of the studies undertaking the two main data mining tasks (i.e. predictive tasks and descriptive tasks) in the building field. Based on the overview, major challenges and future research trends are also discussed.