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
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.
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.
