Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

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.     

基于NARX神经网络的PM2.5/10浓度值预测模型——以咸阳市两寺渡监测站为例

PM_2.5和PM₁₀（记为PM_2.5/10）对空气质量和人类健康有着严重威胁,日益引起国内外的关注,并成为大气污染控制工程中最重要的部分。基于陕西省咸阳市两寺渡监测站的污染物（PM_2.5、PM₁₀、NO₂、NO、NO_x、CO）和相关气象参数的监测数据,建立起基于非线性有源自回归神经网络的预测模型,并分别针对不同预测时间段确定最优网络结构,从而实现了对未来6小时、12小时以及24小时PM_2.5/10浓度的有效预测。实验结果表明：（1）NARX神经网络模型可对未来24小时内的PM_2.5/10污染物浓度进行较为准确的预测;（2）对于PM_2.5/10未来6小时的预测能力优于对12小时、24小时的预测;（3）预测值偏高或偏低的结果与前后时间段内的气象因素及其他污染物浓度变化情况也具有相关性。