【摘要】 准确的短期能耗预测是原油管道能耗管理的重要依据,有助于能耗目标设定、调度优化和机组组合。原油管道能耗主要体现在泵机组上消耗的电能,因此,有必要对原油管道电耗展开准确预测。传统预测方法通常忽略数据噪声干扰,对数据非线性特征的研究也不够深入,上述因素使原油管道能耗预测变得复杂。因此,提出一种将分解技术、分层抽样、改进粒子群算法和反向传播神经网络相结合的混合预测模型,模型由数据预处理、优化、预测和评价4个部分组成。采用数据分解技术去除冗余噪声,提取数据的主要特征;采用分层抽样对数据集进行划分,避免随机抽样引起的样本偏差;将改进粒子群算法优化后的反向传播神经网络作为预测器。针对我国3条原油管道,对提出的模型展开准确性评价,平均绝对百分误差分别为4.02%、3.58%和3.88%。研究表明,相比几种主流机器学习和SPS软件内的能耗预测模块,提出的预测模型具有较高的预测精度和较强的泛化能力,能被用于原油管道短期电耗预测。更多还原

【Abstract】 Accurate short-term energy consumption prediction is crucial to energy management of crude oil pipelines. Based on the prediction results of energy consumption, some vital decisions such as energy consumption target setting, scheduling optimization and unit combination can be implemented effectively. The energy consumption of crude oil pipelines covers all aspects of the pipeline transportation system, among which the electricity consumption of the pump units is the most extensive. The electricity consumption of the pump units by far accounts for the major part of the energy consumption of the crude oil pipelines. Therefore, it is necessary to accurately predict the energy consumption of the pump units, so as to have an overall assessment for the energy consumption of the pipeline system. At present, there is a wealth of methods that can be used to predict the energy consumption of crude oil pipelines. In traditional prediction methods, there are many limitations that make the prediction results deviate from the actual energy consumption. Generally speaking, the neglect of noise interference and the lack of in-depth research on the nonlinear characteristics of the data are the most common problems. The above factors complicate the energy consumption prediction of crude oil pipelines and make the prediction accuracy unsatisfactory. In order to solve the shortcomings of the traditional prediction methods, a novel hybrid prediction method is proposed for the short-term energy consumption prediction. The proposed hybrid method is based on the decomposition technique, stratified sampling, a modified particle swam algorithm and a back-propagation neural network. The proposed model consists of four parts: the data preprocessing module, the optimization module, the prediction module and the evaluation module. The decomposition technique is adopted to eliminate the redundant noise and extract the major features of the original data. The stratified sampling method is used to divide the data set to avoid the sampling bias of random sampling. The back-propagation neural network optimized by the modified particle swarm optimization algorithm is regarded as a predictor. Based on three crude oil pipelines located in China, the proposed prediction model is evaluated by comparing the predicted results with the actual data. The mean absolute percentage errors of the evaluation indicators are 4.02%, 3.58% and 3.88% respectively. Compared with several popular machine learning methods and the prediction modules in SPS software, the proposed prediction method has excellent prediction accuracy and generation ability, which can be used for short-term energy consumption prediction of crude oil pipelines.更多还原