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Open AccessArticle

Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms

1
Department of Culture, Politics and Society, University of Turin, Turin 10100, Italy
2
Interuniversity Department of Regional & Urban Studies and Planning, Politechnic of Turin, Turin 10100, Italy
3
Department of Physics, University of Turin, Turin 10100, Italy
4
Energy Management, University of Turin, Turin 10100, Italy
*
Author to whom correspondence should be addressed.
Energies 2018, 11(5), 1312; https://doi.org/10.3390/en11051312
Received: 10 April 2018 / Revised: 8 May 2018 / Accepted: 10 May 2018 / Published: 21 May 2018
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. View Full-Text
Keywords: Energy Efficiency Indices; data visualization; clustering algorithms; university campus; energy management Energy Efficiency Indices; data visualization; clustering algorithms; university campus; energy management
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MDPI and ACS Style

Cottafava, D.; Sonetti, G.; Gambino, P.; Tartaglino, A. Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms. Energies 2018, 11, 1312. https://doi.org/10.3390/en11051312

AMA Style

Cottafava D, Sonetti G, Gambino P, Tartaglino A. Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms. Energies. 2018; 11(5):1312. https://doi.org/10.3390/en11051312

Chicago/Turabian Style

Cottafava, Dario; Sonetti, Giulia; Gambino, Paolo; Tartaglino, Andrea. 2018. "Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms" Energies 11, no. 5: 1312. https://doi.org/10.3390/en11051312

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