Analysing the Low Quality of the Data in Lighting Control Systems
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.
KeywordsStep Response Lighting System Light Sensor Ambient Intelligence Fuzzy Random Variable
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- 3.de Keyser, R., Ionescu, C.: Modelling and simulation of a lighting control system. Simulation Modelling Practice and Theory (2009), doi:10.1016/j.simpat.2009.10.003Google Scholar
- 6.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)Google Scholar
- 7.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.2008.03.001Google Scholar
- 14.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)Google Scholar
- 16.Sánchez, L., Couso, I., Casillas, J.: Genetic Learning of Fuzzy Rules based on Low Quality Data. Fuzzy Sets and Systems (2009)Google Scholar
- 17.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)Google Scholar
- 18.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)Google Scholar
- 19.Villar, J.R., Otero, A., Otero, J., Sánchez, L.: Taximeter verification with GPS and Soft Computing Techniques. SoftComputing 14(4), 405–418 (2010)Google Scholar