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Gas Demand Forecasting Based on Artificial Neural Network FREE

[+] Author Affiliations
Kazuyoshi Miura

Teikoku Oil Company Ltd.

Ritsuo Sato

NKK Corporation

Paper No. IPC1998-2103, pp. 887-893; 7 pages
doi:10.1115/IPC1998-2103
From:
  • 1998 2nd International Pipeline Conference
  • Volume 2: Design and Construction; Pipeline Automation and Measurement; Environmental Issues; Rotating Equipment Technology
  • Calgary, Alberta, Canada, June 7–11, 1998
  • Conference Sponsors: Pipeline Division
  • ISBN: 978-0-7918-4023-8
  • Copyright © 1998 by ASME

abstract

Teikoku Oil Co. Ltd. (TOC) and NKK Corp. established a joint pilot project in 1994 in order to provide pipeline application and evaluation of NKK’s gas hydraulic simulation engine (GASTRAN) and to co-develop a Demand Forecasting Model (DFC). When the pilot project finished in March 1997, a commercial system, called Support Operation and Monitoring Application of Pipeline Simulator (SMAPS), was installed in TOC’s operation center.

The DFC, which is based on an artificial neural network architecture, has several advantages for sales forecasting especially as several dozen delivery points that have different sales patterns are connected to the pipeline network. The results from DFC can be easily used for scenarios in off-line simulation to predict future pipeline situations when it is attached to the SMAPS system. It automatically assists the pipeline operator by reducing his workload and evaluating operation plans.

Copyright © 1998 by ASME
This article is only available in the PDF format.

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