Abstract
Energy efficiency represents one of the main challenges in the engineering field, i.e., by means of decreasing the energy consumption due to a better design minimising the energy losses. This is particularly true in real world processes in the industry or in business, where the elements involved generate data full of noise and biases. In other fields as lighting control systems, the emergence of new technologies, as the Ambient Intelligence can be, degrades the quality data introducing linguistic values. The presence of low quality data in Lighting Control Systems is introduced through an experimentation step, in order to realise the improvement in energy efficiency that its of managing could afford. In this contribution we propose, as a future work, the use of the novel genetic fuzzy system approach to obtain classifiers and models able to deal with the above mentioned problems.
This research work is been funded by Gonzalez Soriano, S.A. by means of the the CN-08-028-IE07-60 FICYT research project and by Spanish M. of Science and Technology, under the grant TIN2008-06681-C06-04.
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References
Bernal-Agustín, J.L., Dufo-López, R.: Techno-economical optimization of the production of hydrogen from PV-Wind systems connected to the electrical grid. Renewable Energy 35(4), 747–758 (2010)
Couso, I., Sánchez, L.: Higher order models for fuzzy random variables. Fuzzy Sets and Systems 159, 237–258 (2008)
de Keyser, R., Ionescu, C.: Modelling and simulation of a lighting control system. Simulation Modelling Practice and Theory (2009), doi:10.1016/j.simpat.2009.10.003
Doulos, L., Tsangrassoulis, A., Topalis, F.V.: The role of spectral response of photosensors in daylight responsive systems. Energy and Buildings 40(4), 588–599 (2008)
Folleco, A.A., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Identifying Learners Robust to Low Quality Data. Informatica 33, 245–259 (2009)
Gligor, A., Grif, H., Oltean, S.: Considerations on an Intelligent Buildings Management System for an Optimized Energy Consumption. In: Proceedings of the IEEE Conference on Automation, Quality and Testing, Robotics (2006)
Hviid, C.A., Nielsen, T.R., Svendsen, S.: Simple tool to evaluate the impact of daylight on building energy consumption. Solar Energy (2009), doi:10.1016/j.solener.2008.03.001
Houwing, M., Ajah, A.N., Heijnen, P.W., Bouwmans, I., Herder, P.M.: Uncertainties in the design and operation of distributed energy resources: The case of micro-CHP systems. Energy 33(10), 1518–1536 (2008)
Li, D.H.W., Cheung, K.L., Wong, S.L., Lam, T.N.T.: An analysis of energy-efficient light fittings and lighting controls. Applied Energy 87(2), 558–567 (2010)
Luengo, J., Herrera, F.: Domains of competence of fuzzy rule based classification systems with data complexity measures: A case of study using a fuzzy hybrid genetic based machine learning method. Fuzzy Sets and Systems 161, 3–19 (2010)
Martín, J.A., Gil, A.J.: A new heuristic approach for distribution systems loss reduction. Electric Power Systems Research 78(11), 1953–1958 (2008)
Qiao, B., Liu, K., Guy, C.: A Multi-Agent System for Building Control. In: IAT 2006: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology, pp. 653–659. IEEE Computer Society, Los Alamitos (2006)
Sánchez, L., Couso, I.: Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems. IEEE Transactions on Fuzzy Systems 15(4), 551–562 (2007)
Sánchez, L., Otero, J.: Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms. In: Proceedings of the IEEE Internacional Conference on Fuzzy Systems FUZZ-IEEE 2007 (2007)
Sánchez, L., Suárez, M.R., Villar, J.R., Couso, I.: Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data. Internacional Journal of Approximate Reasoning 49, 607–622 (2008)
Sánchez, L., Couso, I., Casillas, J.: Genetic Learning of Fuzzy Rules based on Low Quality Data. Fuzzy Sets and Systems (2009)
Villar, J.R., Pérez, R., de la Cal, E., Sedano, J.: Efficiency in Electrical Heating Systems: An MAS real World Application. In: Demazeau, Y., et al. (eds.) 7th International Conference on PAAMS 2009. AISC, vol. 55, pp. 460–469. Springer, Heidelberg (2009)
Villar, J.R., de la Cal, E., Sedano, J.: A fuzzy logic based efficient energy saving approach for domestic heating systems. Integrated Computer-Aided Engineering 16(2), 151–164 (2007)
Villar, J.R., Otero, A., Otero, J., Sánchez, L.: Taximeter verification with GPS and Soft Computing Techniques. SoftComputing 14(4), 405–418 (2010)
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Villar, J.R., de la Cal, E., Sedano, J., García-Tamargo, M. (2010). Analysing the Low Quality of the Data in Lighting Control Systems. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_51
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DOI: https://doi.org/10.1007/978-3-642-13769-3_51
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