Natural Gas Consumption Forecasting with Kolmogorov–Arnold Networks: A Comparison with MLP

Kürşad Arslan; Emrah Dönmez

doi:10.35377/saucis...1759966

EN

Natural Gas Consumption Forecasting with Kolmogorov–Arnold Networks: A Comparison with MLP

Abstract

Natural gas remains a vital resource for meeting residential heating energy needs, particularly during the winter months. Accurate demand forecasting is essential for maintaining supply-demand balance, optimizing operational costs, and supporting effective energy management. In this study, the natural gas consumption prediction performance of Kolmogorov-Arnold Networks (KAN), a new neural network architecture, was compared with that of the basic model, Multi-Layer Perceptrons (MLP). Both models were trained and tested on the same dataset using monthly consumption data. While MLPs are diversified through the number of neurons, activation functions, and layer configuration, KAN models are configured by modifying B-spline parameters, grid size, and layer structure. The results show that the KAN model achieved the highest R2 value despite having fewer trained parameters. Although some versions of the MLP model yielded lower Mean Absolute Percentage Error (MAPE) values, they fell short of KAN in terms of overall fit. These findings demonstrate the superior ability of KAN to capture nonlinear patterns in energy demand forecasting, offering computational efficiency.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Kürşad Arslan ^*
0000-0001-8477-0819
Türkiye

Emrah Dönmez
0000-0003-3345-8344
Türkiye

Early Pub Date

December 11, 2025

Publication Date

December 29, 2025

Submission Date

August 7, 2025

Acceptance Date

November 3, 2025

Published in Issue

Year 2025 Volume: 8 Number: 4

DOI

https://doi.org/10.35377/saucis...1759966

IZ

https://izlik.org/JA24TX58FH

Cite

RIS / Bibtex

APA

Arslan, K., & Dönmez, E. (2025). Natural Gas Consumption Forecasting with Kolmogorov–Arnold Networks: A Comparison with MLP. Sakarya University Journal of Computer and Information Sciences, 8(4), 773-784. https://doi.org/10.35377/saucis...1759966

AMA

1.Arslan K, Dönmez E. Natural Gas Consumption Forecasting with Kolmogorov–Arnold Networks: A Comparison with MLP. SAUCIS. 2025;8(4):773-784. doi:10.35377/saucis.1759966

Chicago

Arslan, Kürşad, and Emrah Dönmez. 2025. “Natural Gas Consumption Forecasting With Kolmogorov–Arnold Networks: A Comparison With MLP”. Sakarya University Journal of Computer and Information Sciences 8 (4): 773-84. https://doi.org/10.35377/saucis. 1759966.

EndNote

Arslan K, Dönmez E (December 1, 2025) Natural Gas Consumption Forecasting with Kolmogorov–Arnold Networks: A Comparison with MLP. Sakarya University Journal of Computer and Information Sciences 8 4 773–784.

IEEE

[1]K. Arslan and E. Dönmez, “Natural Gas Consumption Forecasting with Kolmogorov–Arnold Networks: A Comparison with MLP”, SAUCIS, vol. 8, no. 4, pp. 773–784, Dec. 2025, doi: 10.35377/saucis...1759966.

ISNAD

Arslan, Kürşad - Dönmez, Emrah. “Natural Gas Consumption Forecasting With Kolmogorov–Arnold Networks: A Comparison With MLP”. Sakarya University Journal of Computer and Information Sciences 8/4 (December 1, 2025): 773-784. https://doi.org/10.35377/saucis. 1759966.

JAMA

1.Arslan K, Dönmez E. Natural Gas Consumption Forecasting with Kolmogorov–Arnold Networks: A Comparison with MLP. SAUCIS. 2025;8:773–784.

MLA

Arslan, Kürşad, and Emrah Dönmez. “Natural Gas Consumption Forecasting With Kolmogorov–Arnold Networks: A Comparison With MLP”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 4, Dec. 2025, pp. 773-84, doi:10.35377/saucis. 1759966.

Vancouver

1.Kürşad Arslan, Emrah Dönmez. Natural Gas Consumption Forecasting with Kolmogorov–Arnold Networks: A Comparison with MLP. SAUCIS. 2025 Dec. 1;8(4):773-84. doi:10.35377/saucis. 1759966