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Long-term energy demand in Malaysia as a function of energy supply: A comparative analysis of Non-Linear Autoregressive Exogenous Neural Networks and Multiple Non-Linear Regression Models

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dc.citedby2
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorMustapa S.I.en_US
dc.contributor.authorMohammad N.en_US
dc.contributor.authorShakeri M.en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid36651549700en_US
dc.contributor.authorid57220108757en_US
dc.contributor.authorid55433849200en_US
dc.date.accessioned2023-05-29T09:05:34Z
dc.date.available2023-05-29T09:05:34Z
dc.date.issued2021
dc.descriptionDecision making; Economics; Energy policy; Energy utilization; Natural gas; Population statistics; Time series; Auto-regressive; Energy demands; Energy model; Final energy; Long-term energy demand; Multiple non linear regressions; Non linear; Non-linear autoregressive exogenous; Per capita; Predictive energy modeling; Neural networksen_US
dc.description.abstractEnergy modeling and forecasting are key to providing insightful energy-related policy decisions that could propel positive economic growth and sustainable development. In this study, the Non-Linear Autoregressive Exogenous Neural Networks (NARX) and Multiple Non-Linear Regression (MNLR) models were employed for modeling the final energy demand per capita in Malaysia. The non-linear relationship between the time-series data such as GDP per capita, population, crude oil supply, natural gas supply, coal and coke supply, hydropower, electricity demand per capita, and the final energy demand per capita were modeled using five NARX and MNLR models refer to as NARX-1, NARX-3, NARX-5, MNLR-1, and MNLR-2. The effect of varying the network time delay and the hidden neurons on the NARX model performance were investigated. The best model configuration was obtained using 1 network delay and 17 hidden neurons. Both the NARX-1 and MNLR-1 models accurately learn the time series data for the prediction of the final energy consumption per capita. With R2 of 0.999 and 0.996 obtained for the NARX-1 and MLNR-1, respectively, the predicted final energy demand per capita was in close agreement with the actual values. Using the NARX-1 and MNLR-1 models, the final energy demand per capita was projected to be 2.3 toe/person and 2.1toe/person, respectively by 2050. The level of importance analysis of the NARX-1 models and the parameter estimates of the MNLR-1 model revealed that an increase in population and natural gas supply had a significant influence on the predicted final energy demand per capita. � 2021 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo100750
dc.identifier.doi10.1016/j.esr.2021.100750
dc.identifier.scopus2-s2.0-85118476264
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118476264&doi=10.1016%2fj.esr.2021.100750&partnerID=40&md5=284bdbe1ccd886da296b7fcf45fe631f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25925
dc.identifier.volume38
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEnergy Strategy Reviews
dc.titleLong-term energy demand in Malaysia as a function of energy supply: A comparative analysis of Non-Linear Autoregressive Exogenous Neural Networks and Multiple Non-Linear Regression Modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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