Low Quality Data Management for Optimising Energy Efficiency in Distributed Agents

Energy efficiency represents one of the main challenges in the engineering field. The benefit of the energy efficiency is twofold: the reduction of the cost owing to the energy consumption and the reduction in 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 may be considered as distributed agents. Moreover, in some fields like building management systems the data are full of noise and biases, and the emergence of new technologies -as the ambient intelligence can be- 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 enemy efficiency in intelligent distributed systems. We will introduce the problem and some of the challenging fields are to be detailed. Finally, a brief review of methods considering the low quality data is related.