Deep learning for estimating building energy consumption

Sustainable Energy, Grids and Networks

Volumn 6, Issue , 2016, Pages 91-99

  Mocanu, Elena ^a   Nguyen, Phuong H ^a   Gibescu, Madeleine ^a   Kling, Wil L ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Artificial Neural Networks; Conditional Restricted Boltzmann Machine; Energy prediction; Factored Conditional Restricted Boltzmann Machine

Indexed keywords

EID: 84962199062     PISSN: None     EISSN: 23524677     Source Type: Journal    
DOI: 10.1016/j.segan.2016.02.005     Document Type: Article

References (35)

1. Costa A., Keane M.M., Torrens J.I., Corry E. Building operation and energy performance: Monitoring, analysis and optimisation toolkit. Appl. Energy 2013, 101:310-316. 10.1016/j.apenergy.2011.10.037.
2. Yang L., Yan H., Lam J.C. Thermal comfort and building energy consumption implications-A review. Appl. Energy 2014, 115:164-173. 10.1016/j.apenergy.2013.10.062.
3. Simoes M., Roche R., Kyriakides E., Suryanarayanan S., Blunier B., McBee K., Nguyen P., Ribeiro P., Miraoui A. A comparison of smart grid technologies and progresses in Europe and the US. IEEE Trans. Ind. Appl. 2012, 48(4):1154-1162. 10.1109/TIA.2012.2199730.
4. Bakirtzis E.A., Simoglou C.K., Biskas P.N., Labridis D.P., Bakirtzis A.G. Comparison of advanced power system operations models for large-scale renewable integration. Electr. Power Syst. Res. 2015, 128:90-99. 10.1016/j.epsr.2015.06.025.
5. M. Krarti, Energy Audit of Building Systems: An Engineering Approach, second ed., Mechanical and Aerospace Engineering Series, 2011.
6. Dounis A.I. Artificial intelligence for energy conservation in buildings. Adv. Build. Energy Res. 2010, 4(1):267-299. 10.3763/aber.2009.0408.
7. Foucquier A., Robert S., Suard F., Stéphan L., Jay A. State of the art in building modelling and energy performances prediction: A review. Renewable Sustainable Energy Rev. 2013, 23:272-288. 10.1016/j.rser.2013.03.004.
8. Xiang Zhao H., Magoulès F. A review on the prediction of building energy consumption. Renewable Sustainable Energy Rev. 2012, 16(6):3586-3592. 10.1016/j.rser.2012.02.049.
9. Fan S., Hyndman R. Short-term load forecasting based on a semi-parametric additive model. IEEE Trans. Power Syst. 2012, 27(1):134-141. 10.1109/TPWRS.2011.2162082.
10. Taylor J.W. Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles. Int. J. Forecast. 2010, 26(4):627-646. 10.1016/j.ijforecast.2010.02.009.
11. Livera A.M.D., Hyndman R.J., Snyder R.D. Forecasting time series with complex seasonal patterns using exponential smoothing. J. Amer. Statist. Assoc. 2011, 106(496):1513-1527. 10.1198/jasa.2011.tm09771.
12. Aydinalp-Koksal M., Ugursal V.I. Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector. Appl. Energy 2008, 85(4):271-296.
13. Hurtado L.A., Nguyen P.H., Kling W.L. Smart grid and smart building inter-operation using agent-based particle swarm optimization. Sustain. Energy, Grids Netw. 2015, 2:32-40. 10.1016/j.segan.2015.03.003.
14. L. Xuemei, D. Lixing, L. Jinhu, X. Gang, L. Jibin, A novel hybrid approach of KPCA and SVM for building cooling load prediction, in: Third International Conference on Knowledge Discovery and Data Mining, 2010. WKDD'10, 2010. pp. 522-526. http://dx.doi.org/10.1109/WKDD.2010.137.
15. Wong S., Wan K.K., Lam T.N. Artificial neural networks for energy analysis of office buildings with daylighting. Appl. Energy 2010, 87(2):551-557.
16. Kalogirou S.A. Artificial neural networks in energy applications in buildings. Int. J. Low-Carbon Technol. 2006, 1(3):201-216.
17. Yang J., Rivard H., Zmeureanu R. On-line building energy prediction using adaptive artificial neural networks. Energy Build. 2005, 37(12):1250-1259.
18. Bengio Y. Learning deep architectures for AI. Found. Trends Mach. Learn. 2009, 2(1):1-127. also published as a book. Now Publishers, 2009. 10.1561/2200000006.
19. Salakhutdinov R., Mnih A., Hinton G. Restricted Boltzmann machines for collaborative filtering. Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2004) 2007, 791-798. ACM, AAAI Press.
20. Taylor G.W., Hinton G.E., Roweis S.T. Two distributed-state models for generating high-dimensional time series. J. Mach. Learn. Res. 2011, 12:1025-1068.
21. E. Mocanu, D. Mocanu, H. Ammar, Z. Zivkovic, A. Liotta, E. Smirnov, Inexpensive user tracking using Boltzmann machines, in: IEEE International Conference on Systems, Man and Cybernetics (SMC), 2014, pp. 1-6. http://dx.doi.org/10.1109/SMC.2014.6973875.
22. V. Mnih, H. Larochelle, G. Hinton, Conditional restricted Boltzmann machines for structured output prediction, in: Proceedings of the International Conference on Uncertainty in Artificial Intelligence, 2011.
23. Jones N. Computer science: The learning machines. Nature 2014, 505(7482):146-148. 10.1038/505146a.
24. Laserson J. From neural networks to deep learning: zeroing in on the human brain. ACM Crossroads 2011, 18(1):29-34.
25. E. Mocanu, P. Nguyen, M. Gibescu, W. Kling, Comparison of machine learning methods for estimating energy consumption in buildings, in: International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2014, pp. 1-6. http://dx.doi.org/10.1109/PMAPS.2014.6960635.
26. Smolensky P. Information processing in dynamical systems: Foundations of harmony theory. Parallel Distributed Processing: Volume 1: Foundations 1987, 194-281. MIT Press, Cambridge. D.E. Rumelhart, J.L. McClelland (Eds.).
27. Hinton G.E. Training products of experts by minimizing contrastive divergence. Neural Comput. 2002, 14(8):1771-1800. 10.1162/089976602760128018.
28. K. Bache, M. Lichman, UCI machine learning repository, 2013. URL: http://archive.ics.uci.edu/ml.
29. [link]. URL: http://www.mathworks.nl/products/neural-network.
30. Marquardt D.W. An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 1963, 11(2):431-441. 10.1137/0111030.
31. Chang C.-C., Lin C.-J. Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2(3):27:1-27:27.
32. Lukoševičius M., Jaeger H. Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 2009, 3(3):127-149.
33. Lukoševičius M. A practical guide to applying echo state networks. Lecture Notes in Computer Science 2012, vol. 7700.
34. Devlin S.J., Gnanadesikan R., Kettenring J.R. Robust estimation and outlier detection with correlation coefficients. Biometrika 1975, 62(3):531-545.
35. Lehmann E.L., Romano J.P. Testing Statistical Hypotheses. Springer Texts in Statistics 2005, Springer, New York. third ed.