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Integrated predictive adaptive control of heating, cooling, ventilation, daylighting and electrical lighting in buildings

机译:建筑物采暖,制冷,通风,采光和电气照明的集成预测自适应控制

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摘要

The present energy consumption of European Buildings is higher than necessary, given the developments in control engineering. Optimization and integration of smart control into building systems can save substantial quantities of energy on a European scale while improving the standards for indoor comfort. Many tools are available for the simulation of one or some of the following aspects:(a)heating, cooling and indoor thermal comfort, (b)ventilation and indoor air quality, (c)daylighting, electrical lighting and light quality, (d) installations, local control and fault detection, (e)Genetic optimized Neuro-Fuzzy control. The interaction between these aspects, however, is very relevant and cannot be neglected. Therefore, and integrated software tool is required. TNO together with the University of Delft develops such an integrated tool. This paper describes the first results of the utilization of this tool and the development of an integrated, predictive, adaptive building system for indoor climate control.
机译:鉴于控制工程的发展,目前欧洲建筑物的能耗高于必要水平。将智能控制优化并集成到建筑系统中,可以在欧洲范围内节省大量能源,同时提高室内舒适性标准。许多工具可用于以下方面中的一个或一些方面的仿真:(a)加热,冷却和室内热舒适度,(b)通风和室内空气质量,(c)日光,电气照明和光质量,(d)安装,本地控制和故障检测,(e)遗传优化的神经模糊控制。但是,这些方面之间的相互作用是非常相关的,不能忽略。因此,需要集成软件工具。 TNO与代尔夫特大学一起开发了这种集成工具。本文介绍了使用该工具的初步结果以及开发用于室内气候控制的集成,可预测的自适应建筑系统的结果。

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