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首页> 外文期刊>International journal of green energy >Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler
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Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

机译：非线性自回归神经网络的柱式冷却器内部空气温度预测

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摘要

The volcanic plate made pillar cooler system is designed for outdoor spaces as a heat exchanging medium and reduces the outcoming air temperature which flows through the exhaust port. This paper proposes the use of artificial neural networks (ANNs) to predict inside air temperature of a pillar cooler. For this purpose, at first, three statistically significant factors (outside temperature, airing and watering) influencing the inside air temperature of the pillar cooler are identified as input parameters for predicting the output (inside air temperature) and then an ANN was employed to predict the output. In addition, 70%, 15% and 15% data was chosen from a previously obtained data set during the field trial of the pillar cooler for training, testing and validation, respectively, and then an ANN was employed to predict inside air temperature. The training (0.99918), testing (0.99799) and validation errors (0.99432) obtained from the model indicate that the artificial neural network model (NARX) may be used to predict inside air temperature of pillar cooler. This study reveals that, an ANN approach can be used successfully for predicting the performance of pillar cooler.
机译：由火山板制成的支柱冷却器系统设计用于室外空间，作为热交换介质，可降低流经排气口的出风温度。本文提出使用人工神经网络（ANN）来预测立柱冷却器的内部空气温度。为此，首先，将影响柱冷却器内部空气温度的三个统计上显着因素（外部温度，通风和浇水）确定为预测输出（内部空气温度）的输入参数，然后使用ANN进行预测输出。此外，在柱式冷却器的现场试验期间，分别从先前获得的数据集中选择了70％，15％和15％的数据进行训练，测试和验证，然后使用ANN预测内部空气温度。从模型获得的训练（0.99918），测试（0.99799）和验证错误（0.99432）表明，人工神经网络模型（NARX）可用于预测立柱冷却器的内部空气温度。这项研究表明，ANN方法可以成功地用于预测立柱冷却器的性能。

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