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The comparison of particle filter and extended Kalman filter in predicting building envelope heat transfer coefficient

机译:粒子滤波与扩展卡尔曼滤波在预测建筑物围护结构传热系数中的比较

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The building envelope heat transfer coefficient is an important measurement of building energy efficiency. The detection of the heat transfer coefficient is always impacted by surrounding environment and noises. Meanwhile it is impractical to accumulate rich enough data as input to calculate the heat transfer coefficient. The Particle Filter (PF) and Extended Kalman Filter(EKF) are employed in this paper in predicting the heat transfer coefficient based on the temperature control box-heat flow model. With the comparison of the two predicted values with the real measured one, the Particle Filter shows high efficiency and better accurate than Extended Kalman Filter. The simulation results show that the accuracy of PF is high. The budget result of PF is more close to the real values. Then the estimated calculation according to Particle Filter is used to calculate wall body heat transfer coefficient.
机译:建筑围护结构的传热系数是建筑节能的重要指标。传热系数的检测始终受周围环境和噪声的影响。同时,积累足够丰富的数据作为计算传热系数的输入是不切实际的。基于温度控制箱-热流模型,采用粒子滤波(PF)和扩展卡尔曼滤波(EKF)来预测传热系数。通过将两个预测值与实际测量值进行比较,粒子滤波器比扩展卡尔曼滤波器具有更高的效率和更好的精度。仿真结果表明,PF的精度较高。 PF的预算结果更接近实际值。然后,根据粒子过滤器进行的估算计算将用于计算壁体的传热系数。

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