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WSN BASED THERMAL MODELING: A NEW INDOOR ENERGY EFFICIENT SOLUTION

机译:基于WSN的热建模:一种新的室内节能解决方案

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In this paper, we proposed to use the Efficient Indoor Thermal Time Constant (EITTC) to characterize the indoor thermal response in old buildings. Accordingly, a low cost, energy-efficient, wide-applicable indoor thermal modeling solution is developed by combining Wireless Sensor Network (WSN) and Artificial Neural Network (ANN). Experiments on both prototype and building room showed consistent results that the combination of WSN and ANN can provide accurate indoor thermal models. A linear approximation of these models makes it possible to estimate the EITTC of building room. Statistical computations confirmed these estimations by showing a strong correlation between the model's predicted EITTC and measured data. Thus the indoor thermal response under different indoor/outdoor conditions can be characterized. Finally, a model based adaptive heating Start/Shut control method is proposed and tested, with which, direct energy saving is achieved.
机译:在本文中,我们建议使用有效的室内热时间常数(EITTC)来表征老建筑中的室内热响应。因此,通过结合无线传感器网络(WSN)和人工神经网络(ANN),开发了一种低成本,节能,可广泛应用的室内热建模解决方案。在样机和建筑房间的实验均显示出一致的结果,即WSN和ANN的组合可以提供准确的室内热模型。这些模型的线性近似使得可以估算建筑物的EITTC。统计计算通过显示模型的预测EITTC与测量数据之间的强相关性,证实了这些估计。因此,可以表征不同室内/室外条件下的室内热响应。最后,提出并测试了一种基于模型的自适应加热启动/关闭控制方法,实现了直接节能。

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