Abstract
In real world processes in the industry or in business, where the elements involved generate data full of noise and biases, improving the energy efficiency represents one of the main challenges. In other fields as lighting control systems, the emergence of new technologies, such as the Ambient Intelligence, also degrades the quality data introducing linguistic values. In this contribution we propose the use of the novel genetic fuzzy system approach to obtain classifiers and models able to manage low quality data to improve the energy efficiency. The problem is introduced through the experimentation to figure out how significant the improvement of managing the low quality data can be.
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Villar, J.R., de la Cal, E., Sedano, J., García, M. (2010). Evaluating the Low Quality Measurements in Lighting Control Systems. In: Corchado, E., Novais, P., Analide, C., Sedano, J. (eds) Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010). Advances in Intelligent and Soft Computing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13161-5_16
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DOI: https://doi.org/10.1007/978-3-642-13161-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13160-8
Online ISBN: 978-3-642-13161-5
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