Prediction of Natural Gas Consumption in Bahçeşehir Using Machine Learning Models

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aut.relation.conferenceAustralasian Conference on Information Systems (ACIS)en_NZ
aut.researcherHutcheson, Catherine
dc.contributor.authorAhmed, Sen_NZ
dc.contributor.authorMadanian, Sen_NZ
dc.contributor.authorMirza, Fen_NZ
dc.contributor.authorZain, Sen_NZ
dc.date.accessioned2022-11-28T22:05:01Z
dc.date.available2022-11-28T22:05:01Z
dc.date.copyright2021en_NZ
dc.date.issued2021en_NZ
dc.description.abstractAccurate prediction of natural gas consumption is of great importance for supply-demand balances and investments. This paper aims to utilize and compare the performance of multiple powerful machine learning algorithms to accurately predict the consumption of natural gas in Bahçeşehir, Istanbul. The utilized algorithms include Linear Regression, Random Forest Regression, Multilayer perceptron with back propagation (MLP) and gradient boosting (XGBoost). The algorithms were evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R Squared to ensure consistency. The final results indicated that XGBoost outperformed MLP by 0.02, Forest Regression and Linear Regression by 0.04 Mean Absolute Error. XGBoost is highly scalable, efficiently reduces compute time and makes optimal use of memory which makes it a more suitable model for prediction. Accurate predictions reduce loss to the economy and ensure a balance between supply and demand.en_NZ
dc.identifier.citationACIS 2020 Proceedings. 12. https://aisel.aisnet.org/acis2020/12
dc.identifier.urihttps://hdl.handle.net/10292/15669
dc.publisherAssociation for Information Systemsen_NZ
dc.relation.urihttps://aisel.aisnet.org/acis2020/12en_NZ
dc.rights© 2020 by authors. This is an open-access article licensed under a Creative Commons Attribution-NonCommercial 3.0 New Zealand, which permits non-commercial use, distribution, and reproduction in any medium, provided the original authors and ACIS are credited.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectForecasting; Machine learning; XGBoost; MLP; Natural gas consumption
dc.titlePrediction of Natural Gas Consumption in Bahçeşehir Using Machine Learning Modelsen_NZ
dc.typeConference Contribution
pubs.elements-id398361
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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