Abstract
The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting of gas consumption for office building heating. Although the accuracy of the forecasting is similar for both the feed-forward and the recurrent network, the removal of features leads to accuracy reduction much earlier for the feed-forward network. The recurrent network can perform well even with 50% of features. This brings significant benefits in scenarios, where the neural network is used as a blackbox model of building consumption, which is called by an optimizer that minimizes the consumption. The reduction of input dimensionality leads to reduction of costs related to measurement equipment, but also costs related to data transfer.
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References
Villar, J.R., González, S., Sedano, J., Corchado, E., Puigpinós, L., de Ciurana, J.: Meta-heuristic improvements applied for steel sheet incremental cold shaping. Memetic Computing 4(4), 249–261 (2012)
Calvo-Rolle, J.L., Corchado, E.: A bio-inspired knowledge system for improving combined cycle plant control tuning. Neurocomputing 126(0), 95–105 (2014)
Macaš, M., Lhotská, L.: Wrapper feature selection significantly improves nonlinear prediction of electricity spot prices. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1171–1174 (2013)
Sarak, H., Satman, A.: The degree-day method to estimate the residential heating natural gas consumption in turkey: a case study. Energy 28(9), 929–939 (2003)
Kalogirou, S.A.: Applications of artificial neural-networks for energy systems. Applied Energy 67(1-2), 17–35 (2000)
Khotanzad, A., Elragal, H., Lu, T.L.: Combination of artificial neural-network forecasters for prediction of natural gas consumption. IEEE Transactions on Neural Networks 11(2), 464–473 (2000)
Kalogirou, S.A., Bojic, M.: Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy 25(5), 479–491 (2000)
Schijndel, A.W.M.V.: HAMLab: Integrated heat air and moisture modeling and simulation. PhD thesis, Eindhoven: Technische Universiteit (2007)
de Wit, M.: HAMBASE: Heat, Air and Moisture Model for Building And Systems Evaluation. Technische Universiteit Eindhoven, Faculteit Bouwkunde (2006)
Elman, J.L.: Finding structure in time. Cognitive Science 14(2), 179–211 (1990)
Mathworks: Neural Network Toolbox for Matlab ver. 2012b (2012)
Moody, J.E.: The effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems. In: NIPS, pp. 847–854. Morgan Kaufmann (1991)
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Macas, M. et al. (2014). Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumption. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_26
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DOI: https://doi.org/10.1007/978-3-319-07995-0_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07994-3
Online ISBN: 978-3-319-07995-0
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