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

Marijana Zekić-Sušac 1 , Rudolf Scitovski 2 ,  and Adela Has 3
  • 1 Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
  • 2 Department of Mathematics, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
  • 3 Faculty of Economics in Osijek, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia


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.

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  • 1. Bagirov, A. M., Ugon, J., Webb, D. (2011). An efficient algorithm for the incremental construction of a piecewise linear classifier. Information Systems, Vol. 36, pp. 782-790.

  • 2. Hsu, D. (2015). Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data. Applied Energy, Vol. 160, pp. 153-163.

  • 3. Kalogirou, S. A. (2006). Artificial neural networks in energy applications in buildings. International Journal of Low-Carbon Technologies, Vol. 1, No. 3, pp. 201-216.

  • 4. Kogan, J. (2007). Introduction to Clustering Large and High-dimensional Data. Cambridge University Press, New York.

  • 5. Mangold, M., Osterbring, M., Wallbaum, H. (2015). Handling data uncertainties when using Swedish energy performance certificate data to describe energy usage in the building stock. Energy and Buildings, Vol. 102, pp. 328-336.

  • 6. Masters, T. (1995). Advanced Algorithms for Neural Networks, A C++ Sourcebook. John Wiley & Sons, New York.

  • 7. Naji, S., Shamshirband, S., Basser, H., Alengaram, U. J., Jumaat, M. Z., Amirmojahedi, M. (2016). Soft computing methodologies for estimation of energy consumption in buildings with different envelope parameters. Energy Efficiency, Vol. 9, No. 2, pp. 435-453.

  • 8. Patterson, M. G. (1996). What is energy efficiency?: Concepts, indicators and methodological issues. Energy Policy, Vol. 24, No. 5, pp. 377-390.

  • 9. Prieto, A., Prieto, B., Martinez Ortigosa, E., Ros, E., Pelayo, F., Ortega, J., Rojas, I. (2016). Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing, Vol. 204, pp. 242-268.

  • 10. Sabo, K., Scitovski, R., Vazler, I., Zekić-Sušac, M. (2011). Mathematical models of natural gas consumption. Energy Conversion and Management, Vol. 52, pp. 1721-1727.

  • 11. Sajter, D. (2017). Methods of evaluating long-term financial effects of energy efficiency projects. Business and Economic Horizons, Vol. 13, No. 3, pp. 295-311.

  • 12. Scitovski, R., Scitovski, S. (2013). A fast partitioning algorithm and its application to 10 earthquake investigation. Computers & Geosciences, Vol. 59, pp. 124-131.

  • 13. Scitovski, R., Zekić-Sušac M., Has A. (2018). Searching for an optimal partition of incomplete data with application in modeling energy efficiency of public buildings, Croatian Operational Research Review, Vol. 9, No. 2, in press.

  • 14. Tofallis, C. (2015). A better measure of relative prediction accuracy for model selection and model estimation. Journal of the Operational Research Society, Vol. 66, No. 8, pp. 1352-1362.

  • 15. Tommerup, H., Rose, J., Svendsen, S. (2007). Energy-efficient houses built according to the energy performance requirements introduced in Denmark in 2006. Energy and Buildings, Vol. 39, No. 10, pp. 1123-1130.

  • 16. Viswanath, P., Babu, V. S. (2009). Rough-DBSCAN: a fast hybrid density based clustering method for large data sets. Pattern Recognition Letters. Vol. 30, pp. 1477-1488.

  • 17. Wang, Z. X., Ding, Y. (2015). An occupant-based energy consumption prediction model for office equipment. Energy and Buildings, Vol. 109, pp. 12-22.

  • 18. Zekić-Sušac, M. (2017). Overview of prediction models for buildings energy efficiency. Proceedings of the 6th International Scientific Symposium Economy Of Eastern Croatia – Vision and Growth, Mašek Tonković A. (Ed.), Faculty of Economics in Osijek, Osijek, May 25-27, 2017, pp. 697-706.

  • 19. Zekić-Sušac, M., Šarlija, A., Has, A., Bilandžić, A. (2016). Predicting company growth using logistic regression and neural networks. Croatian Operational Research Review, Vol. 7, No. 2, pp. 229-248.


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