Demand forecasting at low aggregation levels using Factored Conditional Restricted Boltzmann Machine

19th Power Systems Computation Conference, PSCC 2016

Volumn , Issue , 2016, Pages

  Mocanu, Elena ^a   Nguyen, Phuong H ^a   Gibescu, Madeleine ^a   Larsen, Emil Mahler ^b   Pinson, Pierre ^b  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

Deep Learning; Energy Prediction; Factored Conditional Restricted Boltzmann Machine; Support Vector Machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; ELECTRIC UTILITIES; LEARNING SYSTEMS; METEOROLOGY; PROBLEM SOLVING; STOCHASTIC SYSTEMS; SUPPORT VECTOR MACHINES; TIME SERIES;

BI-DIRECTIONAL CONNECTIONS; CONDITIONAL RESTRICTED BOLTZMANN MACHINES; DEEP LEARNING; ELECTRIC POWER CONSUMPTION; ENERGY PREDICTION; MACHINE LEARNING APPROACHES; NONLINEAR TIME SERIES; TIME SERIES PREDICTION;

FORECASTING;

EID: 84986571440     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PSCC.2016.7540994     Document Type: Conference Paper

References (27)

1. M. Krarti, Energy Audit of Building Systems: An Engineering Approach, Second Edition, ser. Mechanical and Aerospace Engineering Series. Taylor & Francis, 2012.
2. A. Foucquier, S. Robert, F. Suard, L. Stphan, and A. Jay, "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, vol. 23, no. 0, pp. 272-288, 2013.
3. H. xiang Zhao and F. Magouls, "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, vol. 16, no. 6, pp. 3586-3592, 2012.
4. A. I. Dounis, "Artificial intelligence for energy conservation in buildings," Advances in Building Energy Research, vol. 4, no. 1, pp. 267-299, 2010.
5. S. Fan and R. Hyndman, "Short-term load forecasting based on a semi-parametric additive model," IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 1 34-141, Feb 2012.
6. J. W. Taylor, "Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, vol. 26, no. 4, pp. 627-646, 2010.
7. A. M. D. Livera, R. J. Hyndman, and R. D. Snyder, "Forecasting time series with complex seasonal patterns using exponential smoothing," Journal of the American Statistical Association, vol. 106, no. 496, pp. 1513-1527, 2011.
8. M. Aydinalp-Koksal and V. I. Ugursal, "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, vol. 85, no. 4, pp. 271-296, 2008.
9. L. Hurtado, P. Nguyen, and W. Kling, "Smart grid and smart building inter-operation using agent-based particle swarm optimization," Sustainable Energy, Grids and Networks, 2015.
10. E. Mocanu, P. Nguyen, M. Gibescu, and W. Kling, "Comparison of machine learning methods for estimating energy consumption in buildings," in International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), July 2014, pp. 1-6.
11. S. A. Kalogirou, "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, vol. 1, no. 3, pp. 201-216, 2006.
12. S. Wong, K. K. Wan, and T. N. Lam, "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, vol. 87, no. 2, pp. 551-557, 2010.
13. Y. Bengio, "Learning deep architectures for AI," Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1-127, 2009, also published as a book. Now Publishers, 2009.
14. "Ecogrid eu project." [Online]. Available: http://www.eu-ecogrid.net/
15. G. W. Taylor, G. E. Hinton, and S. T. Roweis, 'Two distributed-state models for generating high-dimensional time series," Journal of Machine Learning Research, vol. 12, pp. 1025-1068, 2011.
16. E. Mocanu, D. Mocanu, H. Ammar, Z. Zivkovic, A. Liotta, and E. Smirnov, "Inexpensive user tracking using boltzmann machines," in IEEE International Conference on Systems, Man and Cybernetics (SMC), Oct 2014, pp. 1-6.
17. V. Mnih, H. Larochelle, and G. Hinton, "Conditional restricted boltzmann machines for structured output prediction," in Proceedings of the International Conference on Uncertainty in Artificial Intelligence, 2011.
18. P. Smolensky, "Information processing in dynamical systems: Foundations of harmony theory," in Parallel Distributed Processing: Volume J: Foundations, D. E. Rumelhart, J. L. McClelland et al., Eds. Cambridge: MIT Press, 1987, pp. 194-281.
19. H. Larochelle and Y. Bengio, "Classification using discriminative restricted Boltzmann machines," 2008, pp. 536-543.
20. R. Salakhutdinov, A. Mnih, and G. Hinton, "Restricted boltzmann machines for collaborative filtering," in In Machine Learning, Proceedings of the Twenty-fourth International Conference (ICML 2004). ACM. AAAI Press, 2007, pp. 791-798.
21. G. E. Hinton, "Training Products of Experts by Minimizing Contrastive Divergence," Neural Computation, vol. 14, no. 8, pp. 1771-1800, Aug. 2002.
22. G. Hinton, "A practical guide to training restricted boltzmann machines," in Neural Networks: Tricks of the Trade, ser. Lecture Notes in Computer Science, 2012, vol. 7700, pp. 599-619.
23. E. Larsen, P. Pinson, G. Le Ray, and G. Giannopouios, "Demonstration of market-based real-time electricity pricing on a congested feeder," in 12th International Conference on the European Energy Market (EEM), May 2015, pp. 1-5.
24. B. C. Geiger and G. Kubin, "Signal enhancement as minimization of relevant information loss," CoRR, vol. absI120S.693S, 2012.
25. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, 2006.
26. c.-c. Chang and c.-J. Lin, "Libsvm: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 27:1-27:27, May 2011.
27. A. Foley, P. Leahy, A. Marvuglia, and E. McKeogh, "Current methods and advances in forecasting of wind power generation," Renewable Energy, vol. 37, no. I, pp. 1-8, 2012.