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Evaluating the Low Quality Measurements in Lighting Control Systems

机译:评估照明控制系统中的低质量测量

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摘要

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.
机译:在工业或商业中的实际过程中,其中涉及的元素生成充满噪声和偏差的数据,提高能效是主要挑战之一。在照明控制系统等其他领域,环境智能等新技术的出现也降低了引入语言价值的质量数据。在这项贡献中,我们建议使用新颖的遗传模糊系统方法来获得能够管理低质量数据以提高能源效率的分类器和模型。通过实验引入了该问题,以找出改善管理低质量数据的重要性。

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