Preprint Article Version 1 This version is not peer-reviewed

Explorative Multidimensional Analysis for Energy Efficiency: Dataviz Versus Clustering Algorithms

Version 1 : Received: 10 April 2018 / Approved: 10 April 2018 / Online: 10 April 2018 (10:40:47 CEST)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Energies 2018, 11, 1312
DOI: 10.3390/en11051312

Abstract

In this paper, we propose a simple tool to help the energy management of a large buildings 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. First, we framed the issue of energy performance indicators, and how they feed into data visualization (Data Viz) tools for a large building stock, especially for university campuses. Both for Data Viz and clustering algorithm processes, we discussed two possible approaches to choose the right number of clusters and the identification of alert thresholds and outliers, after a brief presentation of the University of Turin's building stock case study. Different Data Viz tools have been studied to apply a specific clustering algorithm, the k-means one. An explorative analysis based on the general Multidimensional detective approach by Inselberg has been performed. Two multidimensional analysis tools, the Scatter Plot Matrix and the Parallel coordinates method have been used. Secondly, the k-means clustering algorithm has been applied on the same dataset in order to test the hypothesis made during the explorative analysis. Data Viz techniques developed in this study revealed to be very useful to explore quickly and simply a large buildings' stock, identifying the worst efficient buildings and clustering them according to their distinct functions.

Subject Areas

energy efficiency indices; data visualization; clustering algorithms; university campus; energy management

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