THERMAL SCIENCE

International Scientific Journal

SUPPORT VECTOR MACHINE FOR THE PREDICTION OF HEATING ENERGY USE

ABSTRACT
Prediction of a building energy use for heating is very important for adequate energy planning. In this paper the daily district heating use of one university campus was predicted using the support vector machine model. Support vector machine is the artificial intelligence method that has recently proved that it can achieve comparable, or even better prediction results than the much more used artificial neural networks. The proposed model was trained and tested on the real, measured data. The model accuracy was compared with the results of the previously published models (various neural networks and their ensembles) on the same database. The results showed that the support vector machine model can achieve better results than the individual neural networks, but also better than the conventional and multistage ensembles. It is expected that this theoretically well-known methodology finds wider application, especially in prediction tasks.
KEYWORDS
PAPER SUBMITTED: 2017-05-26
PAPER REVISED: 2017-10-25
PAPER ACCEPTED: 2017-12-11
PUBLISHED ONLINE: 2018-04-28
DOI REFERENCE: https://doi.org/10.2298/TSCI170526126S
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2018, VOLUME 22, ISSUE Supplement 4, PAGES [S1171 - S1181]
REFERENCES
  1. Council., E. P. a. Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings. Official Journal of the European Union 2010, L153 (2010), pp. 13-35.
  2. Zhao, H.-x. and Magoulès, F. A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16 (2012), 6, pp. 3586-3592. doi:10.1016/j.rser.2012.02.049
  3. Foucquier, A., Robert, S., Suard, F., Stéphan, L. and Jay, A. State of the art in building modelling and energy performances prediction: A review. Renewable and Sustainable Energy Reviews, 23 (2013), pp. 272-288. doi:10.1016/j.rser.2013.03.004
  4. Kusiak, A., Li, M. and Zhang, Z. A data-driven approach for steam load prediction in buildings. Applied Energy, 87 (2010), 3, pp. 925-933. doi:10.1016/j.apenergy.2009.09.004
  5. Vujić, B., Vukmirović, S., Vujić, G., Jovičić, N., Jovičić, G. and Babić, M. Experimental and artificial neural network approach for forecasting of traffic air pollution in urban areas: the case of subotica. Thermal Science, 14 (2010), suppl., pp. 79-87. doi:10.2298/TSCI100507032V
  6. Ćirić, I. T., Ćojbašić, Ž. M., Nikolić, V. D., Živković, P. M. and Tomić, M. A. Air quality estimation by computational intelligence methodologies. Thermal Science, 16 (2012), suppl. 2, pp. 493-504. doi:10.2298/TSCI120503186C
  7. Özener, O., Yüksek, L. and Özkan, M. Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine. Thermal Science, 17 (2013), 1, pp. 153-166. doi:10.2298/TSCI120321220O
  8. Esen, H., et al., Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system. Energy and Buildings, 40 (2008), 6, pp. 1074-1083. doi:10.1016/j.enbuild.2007.10.002
  9. Kumar, R., Aggarwal, R. and Sharma, J. Energy analysis of a building using artificial neural network: A review. Energy and Buildings, 65 (2013), pp. 352-358.
  10. Jovanović, R. Ž., Sretenović, A. A. and Živković, B. D. Multistage ensemble of feedforward neural networks for prediction of heating energy consumption. Thermal Science (2015) , online first. doi:10.2298/TSCI150122140J
  11. Jovanović, R. Ž., Sretenović, A. A. and Živković, B. D. Ensemble of various neural networks for prediction of heating energy consumption. Energy and Buildings, 94 (2015), pp. 189-199. doi:10.1016/j.enbuild.2015.02.052
  12. Jovanovic, R. and Sretenovic, A. Various multistage ensembles for prediction of heating energy consumption, Modeling, Identification and Control, 36 (2015), 2, pp. 119-132. doi: 10.4173/mic.2015.2.4
  13. Jovanovic, R. and Sretenovic, A. Ensemble of radial basis neural networks with k-means clustering for heating energy consumption prediction. FME Transactions, 44 (2016), 3, pp. 217-223.
  14. Ćirić, I. T., Ćojbašić, Ž. M., Nikolić, V. D., Igić, T. S. and Turšnek, B. A. Intelligent optimal control of thermal vision-based Person-Following Robot Platform. Thermal Science, 18 (2014), 3, pp. 957-966. doi:10.2298/TSCI1403957C
  15. Ekonomou, L. Greek long-term energy consumption prediction using artificial neural networks. Energy, 35 (2010), 2, pp. 512-517. doi:10.1016/j.energy.2009.10.018
  16. Vapnik, V., The nature of statistical learning theory. 2013: Springer Science & Business Media.
  17. Naradasu, K. R., Jyothirmai, S. and Ramesh, R. Towards artificial intelligence based diesel engine performance control under varying operating conditions using support vector regression. Thermal Science, 17 (2013), 1, pp. 167-178. doi:10.2298/TSCI120413218N
  18. Dong, B., Cao C., and Lee S.E., Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings, 37, (2005). pp. 545-553. doi:10.1016/j.enbuild.2004.09.009
  19. Esen, H., Esen, M., and Ozsolak O., Modelling and experimental performance analysis of solar-assisted ground source heat pump system. Journal of Experimental & Theoretical Artificial Intelligence 29, (2017), pp. 1-17. doi:10.1080/0952813X.2015.1056242
  20. Esen, H., et al., Modeling a ground-coupled heat pump system by a support vector machine. Renewable Energy, 33 (2008), 8, pp. 1814-1823. dx.doi.org/10.1016/j.renene.2007.09.025
  21. Esen, H., et al., Modelling of a new solar air heater through least-squares support vector machines, Expert Systems with Applications, 36 (2009), 7, pp. 10673-10682. dx.doi.org/10.1016/j.eswa.2009.02.045
  22. Wang, L., Support Vector Machines: theory and applications, Springer Science & Business Media, 2005
  23. Gunn, S. R. Support vector machines for classification and regression. ISIS technical report, 14 (1998).
  24. Smola, A. J. and Schölkopf, B. A tutorial on support vector regression. Statistics and computing, 14 (2004), 3, pp. 199-222. doi: 10.1023/B:STCO.0000035301.49549.88
  25. Li, Q., Meng, Q., Cai, J., Yoshino, H. and Mochida, A. Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86 (2009), 10, pp. 2249-2256. doi:10.1016/j.apenergy.2008.11.035
  26. Cherkassky, V. and Ma, Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural networks, 17 (2004), 1, pp. 113-126. doi:10.1016/j.apenergy.2008.11.035
  27. Sretenovic, A. Analysis of energy use at university campus. Master, NTNU, Trondheim, 2013.
  28. Guan J., Nord N., and Chen S., Energy planning of university campus building complex: Energy usage and coincidental analysis of individual buildings with a case study. Energy and Buildings, 124, (2016), pp. 99-111. doi:10.1016/j.enbuild.2016.04.051
  29. Chang, C.-C. and Lin, C.-J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2 (2011), 3, pp. 27. doi:10.1145/1961189.1961199
  30. Li, Q., et al., Predicting hourly cooling load in the building: a comparison of support vector machine and different artificial neural networks, Energy Conversion and Management, 50 (2009), 1, pp. 90-96. doi:10.1016/j.enconman.2008.08.033
  31. Paniagua-Tineo, A., et al., Prediction of daily maximum temperature using a support vector regression algorith,. Renewable energy, 36, (2011), 11, pp. 3054-3060. doi:10.1016/j.renene.2011.03.030
  32. Mohandes, M.A., et al., Support vector machines for wind speed prediction, Renewable energy, 29, (2004), 6, pp. 939-947. doi:10.1016/j.renene.2003.11.009

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence