Accesso libero

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

INFORMAZIONI SU QUESTO ARTICOLO

Cita

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. 782-790.10.1016/j.is.2010.12.002Search in Google Scholar

2. Hsu, D. (2015). Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data. Applied Energy, Vol. 160, pp. 153-163.10.1016/j.apenergy.2015.08.126Search in Google Scholar

3. Kalogirou, S. A. (2006). Artificial neural networks in energy applications in buildings. International Journal of Low-Carbon Technologies, Vol. 1, No. 3, pp. 201-216.10.1093/ijlct/1.3.201Search in Google Scholar

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

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. 328-336.10.1016/j.enbuild.2015.05.045Search in Google Scholar

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

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. 435-453.10.1007/s12053-015-9373-zSearch in Google Scholar

8. Patterson, M. G. (1996). What is energy efficiency?: Concepts, indicators and methodological issues. Energy Policy, Vol. 24, No. 5, pp. 377-390.10.1016/0301-4215(96)00017-1Search in Google Scholar

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. 242-268.10.1016/j.neucom.2016.06.014Search in Google Scholar

10. Sabo, K., Scitovski, R., Vazler, I., Zekić-Sušac, M. (2011). Mathematical models of natural gas consumption. Energy Conversion and Management, Vol. 52, pp. 1721-1727.10.1016/j.enconman.2010.10.037Search in Google Scholar

11. Sajter, D. (2017). Methods of evaluating long-term financial effects of energy efficiency projects. Business and Economic Horizons, Vol. 13, No. 3, pp. 295-311.10.15208/beh.2017.22Search in Google Scholar

12. Scitovski, R., Scitovski, S. (2013). A fast partitioning algorithm and its application to 10 earthquake investigation. Computers & Geosciences, Vol. 59, pp. 124-131.10.1016/j.cageo.2013.06.010Search in Google Scholar

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.10.17535/crorr.2018.0020Search in Google Scholar

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. 1352-1362.10.1057/jors.2014.103Search in Google Scholar

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. 1123-1130.10.1016/j.enbuild.2006.12.011Search in Google Scholar

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. 1477-1488.10.1016/j.patrec.2009.08.008Search in Google Scholar

17. Wang, Z. X., Ding, Y. (2015). An occupant-based energy consumption prediction model for office equipment. Energy and Buildings, Vol. 109, pp. 12-22.10.1016/j.enbuild.2015.10.002Search in Google Scholar

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.Search in Google Scholar

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. 229-248.10.17535/crorr.2016.0016Search in Google Scholar