<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Josip Juraj Strossmayer University of Osijek

Croatian Review of Economic, Business and Social Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Although energy efficiency is a hot topic in the context of global climate change, in the European Union directives and in national energy policies,...

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

Pubblicato online: 14 dic 2018

Pagine: 57 - 66

Ricevuto: 01 giu 2018

Accettato: 30 ott 2018

DOI: https://doi.org/10.2478/crebss-2018-0013

Parole chiave
artificial neural networks, clustering, energy efficiency, machine learning, prediction model

© 2018 Marijana Zekić-Sušac et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Pubblicato online: 14 dic 2018

Pagine: 57 - 66

Ricevuto: 01 giu 2018

Accettato: 30 ott 2018

DOI: https://doi.org/10.2478/crebss-2018-0013

Parole chiaveartificial neural networks, clustering, energy efficiency, machine learning, prediction model

© 2018 Marijana Zekić-Sušac et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
artificial neural networks, clustering, energy efficiency, machine learning, prediction model