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首页» 过刊浏览» 2020» Vol.5» Issue(4) 567-577     DOI : 10.3969/j.issn.2096-1693.2020.04.050
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基于机器学习的原油管输能耗预测方法研究
徐磊,侯磊,李雨,张鑫儒,白小众 ,雷婷,朱振宇,刘金海,谷文渊,孙欣
1 中国石油大学(北京)机械与储运工程学院,北京 102249 2 中国石油大学(北京)油气管道输送安全国家工程实验室/石油工程教育部重点实验室,北京 102249 3 国家管网集团北方管道有限责任公司锦州输油气分公司,锦州 121000
Research into prediction of energy consumption of crude oil pipelines based on machine learning
XU Lei, HOU Lei, LI Yu, ZHANG XinRu, BAI Xiaozhong , LEI Ting, ZHU Zhenyu, LIU Jinhai, GU Wenyuan, SUN Xin
1 College of Mechanical and Transportation Engineering, China University of Petroleum-Beijing, Beijing 102249, China 2 National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China 3 Jinzhou Oil and Gas Transmission Branch, National Pipe Network Group, Northern Pipeline Co., Ltd., Jinzhou 121000, China

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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。
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关键词 : 能耗预测;原油管道;分解技术;机器学习;反向传播神经网络
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 meth   ods, 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.  


Key words: energy consumption prediction; crude oil pipeline; decomposition technique; machine learning method; back-propagation neural networks
收稿日期: 2020-12-30     
PACS:    
基金资助:国家重点研发计划项目“油气长输管道及储运设施检验评价与安全保障技术”(2016YFC0802100) 资助
通讯作者: houleicup@126.com
引用本文:   
XU Lei, HOU Lei, LI Yu, ZHANG XinRu, BAI Xiaozhong, LEI Ting, ZHU Zhenyu, LIU Jinhai, GU Wenyuan, SUN Xin. Research into prediction of energy consumption of crude oil pipelines based on machine learning. Petroleum Science Bulletin, 2020, 04: 567-577.
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