Fault detection analysis using data mining techniques for a cluster of smart office buildings. Issue 9 (1st June 2015)

• Record Type: Journal Article
• Authors: Capozzoli, Alfonso; Lauro, Fiorella; Khan, Imran
• Is Part Of: Expert systems with applications. Volume 42:Issue 9(2015)
• Page Start: 4324
• Page End: 4338
• Publication Date: 2015-06-01
• Subjects: Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.01.010
• Languages: English
• ISSNs: 0957-4174

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st090">Highlights</title> <sec> <p id="sp0005"> <list id="l0005"> <list-item id="o0005"> <label></label> <p id="p0290">An energy fault detection analysis was performed for a cluster of buildings.</p> </list-item> <list-item id="o0010"> <label></label> <p id="p0295">Pattern recognition techniques coupled with outliers detection methods were used.</p> </list-item> <list-item id="o0015"> <label></label> <p id="p0300">Anomalies are detected during early morning, lunch break, and end of working hours.</p> </list-item> <list-item id="o0020"> <label></label> <p id="p0305">The methodology can be easily implemented in BEMS.</p> </list-item> </list> </p> </sec> </abstract>