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Predicting energy consumption of multiproduct pipeline using artificial neural networks

Author

Listed:
  • Zeng, Chunlei
  • Wu, Changchun
  • Zuo, Lili
  • Zhang, Bin
  • Hu, Xingqiao

Abstract

In this paper artificial neural network is introduced to forecast the daily electricity consumption of a multiproduct pipeline which is used to drive oil pumps. Forecasting electricity energy consumption is complicated since there are so many parameters affecting the energy consumption. Two different sets of input vectors are selected from these parameters by detailed analysis of energy consumption in this study, and two corresponding multilayer perceptron artificial neural network (MLP ANN) models are developed. To enhance the generalization ability, the numbers of hidden layers and neurons, activation functions and training algorithm of each model are optimized by the trial-and-error process step by step. The performances of the two proposed MLP ANN models are evaluated on real data of a Chinese multiproduct pipeline, and compared with two linear regression and two support vector machine (SVM) models which are produced using different inputs. Results show that the two MLP ANN models have very high accuracy for prediction and better forecasting performance than the other models. The proposed input vectors and MLP ANN models are useful not only in the effective evaluation of batch scheduling and pumping operation, but also in the energy consumption target setting.

Suggested Citation

  • Zeng, Chunlei & Wu, Changchun & Zuo, Lili & Zhang, Bin & Hu, Xingqiao, 2014. "Predicting energy consumption of multiproduct pipeline using artificial neural networks," Energy, Elsevier, vol. 66(C), pages 791-798.
    • Handle: RePEc:eee:energy:v:66:y:2014:i:c:p:791-798
    DOI: 10.1016/j.energy.2014.01.062
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    12. Hu, Gang & Xu, Zhaoqiang & Wang, Guorong & Zeng, Bin & Liu, Yubing & Lei, Ye, 2021. "Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression," Energy, Elsevier, vol. 224(C).
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