Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach
Zekić-Sušac Marijana (),
Scitovski Rudolf () and
Has Adela ()
Additional contact information
Zekić-Sušac Marijana: Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
Scitovski Rudolf: Department of Mathematics, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
Has Adela: Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
Croatian Review of Economic, Business and Social Statistics, 2018, vol. 4, issue 2, 57-66
Although energy efficiency is a hot topic in the context of global climate change, in the European Union directives and in national energy policies, methodology for estimating energy efficiency still relies on standard techniques defined by experts in the field. Recent research shows a potential of machine learning methods that can produce models to assess energy efficiency based on available previous data. In this paper, we analyse a real dataset of public buildings in Croatia, extract their most important features based on the correlation analysis and chi-square tests, cluster the buildings based on three selected features, and create a prediction model of energy efficiency for each cluster of buildings using the artificial neural network (ANN) methodology. The main objective of this research was to investigate whether a clustering procedure improves the accuracy of a neural network prediction model or not. For that purpose, the symmetric mean average percentage error (SMAPE) was used to compare the accuracy of the initial prediction model obtained on the whole dataset and the separate models obtained on each cluster. The results show that the clustering procedure has not increased the prediction accuracy of the models. Those preliminary findings can be used to set goals for future research, which can be focused on estimating clusters using more features, conducted more extensive variable reduction, and testing more machine learning algorithms to obtain more accurate models which will enable reducing costs in the public sector.
Keywords: artificial neural networks; clustering; energy efficiency; machine learning; prediction model (search for similar items in EconPapers)
JEL-codes: C52 C53 C55 F64 (search for similar items in EconPapers)
References: Add references at CitEc
Citations Track citations by RSS feed
Downloads: (external link)
https://www.degruyter.com/view/j/crebss.2018.4.iss ... -0013.xml?format=INT (text/html)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:vrs:crebss:v:4:y:2018:i:2:p:57-66:n:7
Access Statistics for this article
Croatian Review of Economic, Business and Social Statistics is currently edited by Dragan Bagić, Ksenija Dumičić and Nataša Erjavec
More articles in Croatian Review of Economic, Business and Social Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().