Abstract
In this work several approaches to prediction of natural gas consumption with neural and fuzzy neural systems are analyzed and tested. The data covers daily natural gas load in a certain region of Poland. Prediction strategies tested in the paper include: single neural net module approach, combination of three neural modules, temperature clusterization based method, and application of fuzzy neural networks. The results indicate the superiority of temperature clusterization based method over modular and fuzzy neural approaches. One of the interesting issues observed in the paper is relatively good performance of tested methods in the case of a long-term (four week) prediction compared to mid-term (one week) prediction. Generally, the .results are significantly better than those obtained by statistical methods currently used for this task in the gas company under consideration.
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Viet, N.H., Mańdziuk, J. (2003). Prediction of natural gas consumption with feed-forward and fuzzy neural networks. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_21
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DOI: https://doi.org/10.1007/978-3-7091-0646-4_21
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-00743-3
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