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Applying Neuro-Fuzzy Model in Indoor Comfort Microclimate Control

机译：神经模糊模型在室内舒适微气候控制中的应用

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This paper proposes a concept for managing climate control systems (HVAC systems) in large commercial buildings based on predictive models. The proposed control concept reduces the consumption of gas used for room heating while maintaining the acceptable range of the required temperature. The authors propose fuzzy and neuro-fuzzy models to ensure comfort temperature and humidity in the indoor environment, and thus minimize energy consumption. The management strategy is formed using predictive models. The developed management strategy is applied to the climate control system through a hardware and software complex consisting of a client connected to the system and a server that forecasts changes in indoor temperature, gas consumption and forms a management strategy. The tests that implemented the proposed concept were performed in a commercial building. The efficiency of the proposed concept is shown in comparison with the control algorithm built into the HVAC system.
机译：本文提出了一种基于预测模型的大型商业建筑气候控制系统（HVAC系统）管理概念。所提出的控制概念减少了用于房间供暖的气体消耗，同时保持了所需温度的可接受范围。作者提出了模糊和神经模糊的模型，以确保室内环境中舒适的温度和湿度，从而最大程度地减少能耗。管理策略是使用预测模型形成的。所开发的管理策略通过包括连接到系统的客户端和预测室内温度，气体消耗量变化并形成管理策略的服务器的硬件和软件复合体应用于气候控制系统。实施提议的概念的测试是在商业建筑中进行的。与内置在HVAC系统中的控制算法相比，显示了所提出概念的效率。

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