Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms
Dario Cottafava,
Giulia Sonetti,
Paolo Gambino and
Andrea Tartaglino
Additional contact information
Dario Cottafava: Department of Culture, Politics and Society, University of Turin, Turin 10100, Italy
Giulia Sonetti: Interuniversity Department of Regional & Urban Studies and Planning, Politechnic of Turin, Turin 10100, Italy
Paolo Gambino: Department of Physics, University of Turin, Turin 10100, Italy
Andrea Tartaglino: Energy Management, University of Turin, Turin 10100, Italy
Energies, 2018, vol. 11, issue 5, 1-18
Abstract:
We propose a simple tool to help the energy management of a large building stock defining clusters of buildings with the same function, setting alert thresholds for each cluster, and easily recognizing outliers. The objective is to enable a building management system to be used for detection of abnormal energy use. We start reviewing energy performance indicators, and how they feed into data visualization (DataViz) tools for a large building stock, especially for university campuses. After a brief presentation of the University of Turin’s building stock which represents our case study, we perform an explorative analysis based on the Multidimensional Detective approach by Inselberg, using the Scatter Plot Matrix and the Parallel Coordinates methods. The k-means clustering algorithm is then applied on the same dataset to test the hypotheses made during the explorative analysis. Our results show that DataViz techniques provide quick and user-friendly solutions for the energy management of a large stock of buildings. In particular, they help identifying clusters of buildings and outliers and setting alert thresholds for various Energy Efficiency Indices.
Keywords: Energy Efficiency Indices; data visualization; clustering algorithms; university campus; energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)
Downloads: (external link)
https://www.mdpi.com/1996-1073/11/5/1312/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/5/1312/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:5:p:1312-:d:148189
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().