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Application of Elman Neural Network with HP Filter in the Trend Supply of Self-provided Power Plant Forecasting in the Iron and Steel Industry

机译:ELMAN神经网络在钢铁工业中自信电厂预测趋势供应中的应用

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Aiming at the power plant energy consumption and gas balance influenced serious with the affluent gas fluctuate frequently of byproduct gas system in an iron and steel industry, which is very difficult to be modeled using the mechanism modeling, a forecast trend sequence of the gas supply HP-ENN model was established based on the characteristics of self-provided power plant energy utilization and the properties of HP filter, Elman neural network. The prediction results using practical production data show that using the proposed HP-Elman method that sample A 48, 60 points trend forecast average relative error are 0.37%, 0.47% and sample B 48, 60 points trend forecast average relative error are 0.82%, 1.03%, which can effectively for the trend forecast of self-provided power plant gas supply with a reliable prediction capacity.
机译:针对电厂的能量消耗和气体平衡受到钢铁工业中副产品气体系统经常受到富裕气体的严重影响,这是使用机制建模的预测趋势序列非常难以进行建模的副产品气体系统。 -enn模型是基于自助式发电厂能量利用的特点和HP滤波器,Elman神经网络的特性建立。使用实用生产数据的预测结果表明,使用所提出的HP-ELMAN方法,采样48,60点趋势预测平均相对误差为0.37%,0.47%和样本B 48,60点趋势预测平均相对误差为0.82%, 1.03%,可以有效地实现自助式发电厂供气的趋势预测,具有可靠的预测能力。

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