Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression

Gang Hu, Zhaoqiang Xu, Guorong Wang, Bin Zeng, Yubing Liu and Ye Lei

Energy, 2021, vol. 224, issue C

Abstract: Predicting the energy consumption of oil pipelines is an important part of pipeline companies’ energy-saving and consumption-reduction plans and the realization of refined management. In order to predict the energy consumption of the long-distance product oil pipeline faster and better, this manuscript innovatively uses the normal distribution function to improve the search mode of the fruit fly optimization algorithm (FOA). It establishes the normal distribution fruit fly optimization algorithm (NFOA). It enhances search accuracy in the central area and effectively expands the search scope. Experimental results show that the accuracy and stability of the algorithm are improved by 100% and 900%. Then, NFOA combined with support vector regression (NFOA-SVR) is used to predict the three long-distance product pipeline data sets in China. The results show that the optimization speed and prediction accuracy of NFOA-SVR in LCY-Others set and LW-total set are significantly better than the other two algorithms. In the LCY-Pump set, NFOA-SVR has the same accuracy as the other two algorithms. Finally, experiments on random data sets show that the accuracy and stability of NFOA-SVR gradually decrease with the increase of the standard deviation of the data set.

Keywords: Product oil pipeline; Energy consumption prediction; Normal distribution; Fruit fly algorithm; Support vector regression (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221004023
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:224:y:2021:i:c:s0360544221004023

DOI: 10.1016/j.energy.2021.120153

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().