Discovering Knowledge from a Residential Building Stock through Data Mining Analysis for Engineering Sustainability. (December 2015)

• Record Type: Journal Article
• Authors: Capozzoli, Alfonso; Grassi, Daniele; Piscitelli, Marco Savino; Serale, Gianluca
• Is Part Of: Energy procedia. Volume 83(2015)
• Page Start: 370
• Page End: 379
• Publication Date: 2015-12
• Subjects: Residential building stock -- Datamining: Classification tree -- Energy sustainability; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2015.12.212
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: In this paper, a dataset of 92, 906 dwellings was analysed adopting data mining techniques for the classification of heating and domestic hot water primary energy demand and for the evaluation of the most influencing factors. The sample was classified in three energy demand categorical variables (Low, Medium, High) considering different geometrical and physical attributes. The output of the model made it possible to set reference threshold values among the physical variables. Moreover, high energy demand dwellings were analysed in depth using a k-means algorithm in order to evaluate the design variables which need to be considered in a refurbishment process.