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Original scientific paper
https://doi.org/10.2478/crebss-2018-0013

Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach

Marijana Zekić-Sušac ; Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
Rudolf Scitovski   ORCID icon orcid.org/0000-0002-7386-5991 ; Department of Mathematics, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
Adela Has ; Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia

Fulltext: english, pdf (533 KB) pages 57-66 downloads: 449* cite
APA 6th Edition
Zekić-Sušac, M., Scitovski, R. & Has, A. (2018). Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach. Croatian Review of Economic, Business and Social Statistics, 4 (2), 57-66. https://doi.org/10.2478/crebss-2018-0013
MLA 8th Edition
Zekić-Sušac, Marijana, et al. "Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach." Croatian Review of Economic, Business and Social Statistics, vol. 4, no. 2, 2018, pp. 57-66. https://doi.org/10.2478/crebss-2018-0013. Accessed 24 Sep. 2021.
Chicago 17th Edition
Zekić-Sušac, Marijana, Rudolf Scitovski and Adela Has. "Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach." Croatian Review of Economic, Business and Social Statistics 4, no. 2 (2018): 57-66. https://doi.org/10.2478/crebss-2018-0013
Harvard
Zekić-Sušac, M., Scitovski, R., and Has, A. (2018). 'Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach', Croatian Review of Economic, Business and Social Statistics, 4(2), pp. 57-66. https://doi.org/10.2478/crebss-2018-0013
Vancouver
Zekić-Sušac M, Scitovski R, Has A. Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach. Croatian Review of Economic, Business and Social Statistics [Internet]. 2018 [cited 2021 September 24];4(2):57-66. https://doi.org/10.2478/crebss-2018-0013
IEEE
M. Zekić-Sušac, R. Scitovski and A. Has, "Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach", Croatian Review of Economic, Business and Social Statistics, vol.4, no. 2, pp. 57-66, 2018. [Online]. https://doi.org/10.2478/crebss-2018-0013

Abstracts
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

Hrčak ID: 209781

URI
https://hrcak.srce.hr/209781

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