Abstract
The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting of thermal comfort for office building heated by gas. 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 less than 50% of features. This brings significant benefits in scenarios, where the neural network is used as a blackbox model of thermal comfort, which is called by an optimizer that minimizes the deviance from a target value. The reduction of input dimensionality can lead to reduction of costs related to measurement equipment, data transfer and also computational demands of optimization.
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References
Elman, J.L.: Finding structure in time. Cognitive Science 14(2), 179–211 (1990)
de Jesús Rubio, J.: Evolving intelligent algorithms for the modelling of brain and eye signals. Applied Soft Computing 14, Part B, 259–268 (2014)
Macas, M., Lauro, F., Moretti, F., Pizzuti, S., Annunziato, M., Fonti, A., Comodi, G., Giantomassi, A.: Sensitivity based feature selection for recurrent neural network applied to forecasting of heating gas consumption. In: de la Puerta, J.G., et al. (eds.) International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. AISC, vol. 299, pp. 259–268. Springer, Heidelberg (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)
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)
Schijndel, A.W.M.V.: HAMLab: Integrated heat air and moisture modeling and simulation. Ph.D. thesis, Technische Universiteit, Eindhoven (2007), http://archbps1.campus.tue.nl/bpswiki/index.php/Hamlab
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)
de Wit, M.: HAMBASE: Heat, Air and Moisture Model for Building and Systems Evaluation. Technische Universiteit Eindhoven, Faculteit Bouwkunde (2006)
de Wit, M.: Calculation of the predicted mean vote (pmv) and the predicted percentage of dissatisfied (ppd) according Fanger. Online Matlab SW (1998)
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Macas, M. et al. (2014). Importance of Feature Selection for Recurrent Neural Network Based Forecasting of Building Thermal Comfort. In: Bouchachia, A. (eds) Adaptive and Intelligent Systems. ICAIS 2014. Lecture Notes in Computer Science(), vol 8779. Springer, Cham. https://doi.org/10.1007/978-3-319-11298-5_2
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DOI: https://doi.org/10.1007/978-3-319-11298-5_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11297-8
Online ISBN: 978-3-319-11298-5
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