Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms

Dario Cottafava,Andrea Tartaglino,Giulia Sonetti,Paolo Gambino

doi:10.3390/en11051312

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.

Highlights

Energy efficiency programs as well as policies for the reduction of greenhouse gas (GHG)emissions have been adopted worldwide by national governments, international organizations, and public administrations [1]
The clusters hypothesis has been made based on main building function and it has been verified via two methodologies for the identification of buildings clusters: a data visualization approach and a clustering algorithm
Depts., Scientific Buildings and Administrative Offices. This data visualization approach offers a simple way to identify outliers and to set alerts energy consumption thresholds for each buildings function, but the reasons for the inefficiency have to be explained with deeper analyses

Summary

Introduction

Energy efficiency programs as well as policies for the reduction of greenhouse gas (GHG)emissions have been adopted worldwide by national governments, international organizations, and public administrations [1]. The reduction of energy consumption and the shift toward a more sustainable use of resources are increasingly becoming a challenge for any sector and activity related to the built environment [2]. The buildings sector is a high energy-consumer, accounting for over one-third of the global final energy consumption [3]. Energy demand is expected to rise by 50% by 2050 if no action is urgently taken [4]. This means that major efforts are required to go beyond existing technical and economic barriers for improving the efficiency of our energy use in buildings. The power to characterize the energy consumption of a complex building stock, for instance, can reduce cost barriers for energy efficient solutions. The improvement of reliable indicators to measure building energy performance at a neighbourhood/city scale is an important contribution for achieving urban sustainability targets [5,6]

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Discussion

Conclusion