基于混合BP神经网络的原油管道电耗预测研究

原油管道电耗的准确预测能够用于控制原油管道耗能水平，充分挖掘原油管道输送系统的节能潜力。实际采集到的原油管道运行数据具有波动范围大的特点，且存在严重的噪声干扰和信息冗余，对精确预测管道电耗造成不良影响。为解决上述问题，提出一种基于混合神经网络的电耗预测模型。利用自适应噪声的完备集成经验模态分解，对原油管道日运行数据进行分解；利用主成分分析对分解后数据做降维处理；利用改进粒子群算法调节神经网络结构参数；使用该模型预测某原油管道电耗，并与常见的几种预测模型展开对比。结果表明，分解算法能够提高模型预测精度；该混合神经网络模型预测精度最高，其测试集的平均绝对误差为5.394%，较使用分解算法前降低39.200%。

Power Consumption Prediction Method for Crude Oil Pipeline Based on Hybrid BP Neural Network

Accurately predicting the power consumption of crude oil pipelines is conducive to controlling the energy consumption level of such pipelines and fully tapping the energy saving potential of crude oil pipeline transportation systems. Actual operation data of such pipelines have the characteristics of large fluctuation range serious noise interference, and information redundancy, which affect the accurate prediction of pipeline power consumption. To solve these problems, this paper proposes a power consumption prediction model based on a hybrid neural network. The daily operation data of crude oil pipelines are decomposed by complete ensemble empirical mode decomposition with adaptive noise. Principal component analysis is performed to reduce the dimensions of the decomposed data. The improved particle swarm optimization algorithm is applied to adjust the structural parameters of the neural network. The proposed model is applied to predict the power consumption of a crude oil pipeline and compared with some common prediction models. The results show that the decomposition algorithm can improve the prediction accuracy of the model. The hybrid neural network model has the highest prediction accuracy. The average absolute error of the test set is 5.394%, which is 39.200% lower than that before the decomposition algorithm is used.