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

Energy 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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Bamidele Victor Ayodele, Siti Indati Mustapa, Norsyahida Mohammad, Mohammad Shakeri
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Non-linear autoregressive exogenous Artificial neural networks Long-term energy demand Multiple non-linear regression Predictive energy modeling Energy industries. Energy policy. Fuel trade HD9502-9502.5
Acceso en línea:	https://doaj.org/article/43a0449ddf60480f9176151f2be00b99
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:43a0449ddf60480f9176151f2be00b99
record_format	dspace
spelling	oai:doaj.org-article:43a0449ddf60480f9176151f2be00b992021-11-04T04:30:18ZLong-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 Models2211-467X10.1016/j.esr.2021.100750https://doaj.org/article/43a0449ddf60480f9176151f2be00b992021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2211467X21001358https://doaj.org/toc/2211-467XEnergy 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.Bamidele Victor AyodeleSiti Indati MustapaNorsyahida MohammadMohammad ShakeriElsevierarticleNon-linear autoregressive exogenousArtificial neural networksLong-term energy demandMultiple non-linear regressionPredictive energy modelingEnergy industries. Energy policy. Fuel tradeHD9502-9502.5ENEnergy Strategy Reviews, Vol 38, Iss , Pp 100750- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Non-linear autoregressive exogenous Artificial neural networks Long-term energy demand Multiple non-linear regression Predictive energy modeling Energy industries. Energy policy. Fuel trade HD9502-9502.5
spellingShingle	Non-linear autoregressive exogenous Artificial neural networks Long-term energy demand Multiple non-linear regression Predictive energy modeling Energy industries. Energy policy. Fuel trade HD9502-9502.5 Bamidele Victor Ayodele Siti Indati Mustapa Norsyahida Mohammad Mohammad Shakeri 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
description	Energy 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.
format	article
author	Bamidele Victor Ayodele Siti Indati Mustapa Norsyahida Mohammad Mohammad Shakeri
author_facet	Bamidele Victor Ayodele Siti Indati Mustapa Norsyahida Mohammad Mohammad Shakeri
author_sort	Bamidele Victor Ayodele
title	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
title_short	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
title_full	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
title_fullStr	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
title_full_unstemmed	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
title_sort	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
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/43a0449ddf60480f9176151f2be00b99
work_keys_str_mv	AT bamidelevictorayodele longtermenergydemandinmalaysiaasafunctionofenergysupplyacomparativeanalysisofnonlinearautoregressiveexogenousneuralnetworksandmultiplenonlinearregressionmodels AT sitiindatimustapa longtermenergydemandinmalaysiaasafunctionofenergysupplyacomparativeanalysisofnonlinearautoregressiveexogenousneuralnetworksandmultiplenonlinearregressionmodels AT norsyahidamohammad longtermenergydemandinmalaysiaasafunctionofenergysupplyacomparativeanalysisofnonlinearautoregressiveexogenousneuralnetworksandmultiplenonlinearregressionmodels AT mohammadshakeri longtermenergydemandinmalaysiaasafunctionofenergysupplyacomparativeanalysisofnonlinearautoregressiveexogenousneuralnetworksandmultiplenonlinearregressionmodels
_version_	1718445269772664832

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

Ejemplares similares