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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM24908

FORCASTING NATURAL GAS PRICE PREDICTION USING MACHINE LEARNING

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM24908

Volume:

7 |

7 |

Jul 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

HBG

Harish B G

CkGS

Chetan kumar G S

RR

Raghavendrareddy Radder

MK

Manoj K

SN

Shivarajgouda NB

SRS

Soniya R S

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Abstract

In this paper, we will use statistical modeling techniques to forecast natural gas prices in US markets. We will investigate the application and contrast of the decision-tree and random forest linear regression models. Using a total of 5800 records, we used a dataset of natural gas prices in the US expressed as US dollars per Btu from 1998 to 2020. Random forest conducts regression using ensemble learning techniques since it is a supervised learning algorithm. Random forest will forecast the price by utilizing the current prices and comparing them, which will be useful for the user to determine the risk in bidding prices. The results are indicated with up to 97% accuracy. The stage has been prepared for the potential new opportunities that these technologies may present.

KEYWORDS

Random Forest Decision Tree Prediction Natural Gas.

Cite This Article

Harish B G, Chetan kumar G S, Raghavendrareddy Radder, Manoj K, Shivarajgouda NB, Soniya R S, "FORCASTING NATURAL GAS PRICE PREDICTION USING MACHINE LEARNING," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 7, Jul 2023. https://doi.org/10.55041/IJSREM24908

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Publication Date

7/30/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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