Forecasting CO2Emissions in Saudi Arabia Using Artificial Neural Network, Holt-Winters Exponential Smoothing, and Autoregressive Integrated Moving Average Models

Teg Alam; Ali Alarjani

doi:10.1109/ICT-PEP53949.2021.9601031

Forecasting CO₂Emissions in Saudi Arabia Using Artificial Neural Network, Holt-Winters Exponential Smoothing, and Autoregressive Integrated Moving Average Models

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Forecasting models are critical tools for achieving economic development and policy-making in a country. The main goal of this study is to forecast CO2 emissions in the kingdom of Saudi Arabia. In this study, Saudi Arabia's CO2 emissions are predicted using models of the Artificial Neural Network (ANN), Holt-Winters Exponential Smoothing (H-W), and Autoregressive Integrated Moving Average (ARIMA). This research uses statistical software to forecast time series data using ANN, H-W, and ARIMA models on the Kingdom of Saudi Arabia's CO2 emissions from 1960 to 2014. In addition, this study shows the forecast model accuracy using various accuracy measures. The ARIMA (2,1,2) model is found to be suitable for predicting the CO2 emissions of the Kingdom of Saudi Arabia. This study also aims to clarify the current state of CO2 emissions. This study will assist the researcher in better understanding CO2 emission forecasts. In addition, government entities can use the findings of this study to establish strategic plans.

Original language	English
Title of host publication	ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power
Subtitle of host publication	Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	125-129
Number of pages	5
ISBN (Electronic)	9781665416412
DOIs	https://doi.org/10.1109/ICT-PEP53949.2021.9601031
State	Published - 2021
Event	3rd International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2021 - Yogyakarta, Indonesia Duration: 29 Sep 2021 → 30 Sep 2021

Publication series

Name	ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings

Conference

Conference	3rd International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2021
Country/Territory	Indonesia
City	Yogyakarta
Period	29/09/21 → 30/09/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 13 Climate Action

Keywords

Artificial neural networks (ANN)
Autoregressive integrated moving average (ARIMA)
COemissions
Forecasting
Holt-winters exponential smoothing (H-W)

Access to Document

10.1109/ICT-PEP53949.2021.9601031

Cite this

Alam, T., & Alarjani, A. (2021). Forecasting CO₂Emissions in Saudi Arabia Using Artificial Neural Network, Holt-Winters Exponential Smoothing, and Autoregressive Integrated Moving Average Models. In ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings (pp. 125-129). (ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICT-PEP53949.2021.9601031

Alam, Teg ; Alarjani, Ali. / Forecasting CO₂Emissions in Saudi Arabia Using Artificial Neural Network, Holt-Winters Exponential Smoothing, and Autoregressive Integrated Moving Average Models. ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 125-129 (ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings).

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title = "Forecasting CO2Emissions in Saudi Arabia Using Artificial Neural Network, Holt-Winters Exponential Smoothing, and Autoregressive Integrated Moving Average Models",

abstract = "Forecasting models are critical tools for achieving economic development and policy-making in a country. The main goal of this study is to forecast CO2 emissions in the kingdom of Saudi Arabia. In this study, Saudi Arabia's CO2 emissions are predicted using models of the Artificial Neural Network (ANN), Holt-Winters Exponential Smoothing (H-W), and Autoregressive Integrated Moving Average (ARIMA). This research uses statistical software to forecast time series data using ANN, H-W, and ARIMA models on the Kingdom of Saudi Arabia's CO2 emissions from 1960 to 2014. In addition, this study shows the forecast model accuracy using various accuracy measures. The ARIMA (2,1,2) model is found to be suitable for predicting the CO2 emissions of the Kingdom of Saudi Arabia. This study also aims to clarify the current state of CO2 emissions. This study will assist the researcher in better understanding CO2 emission forecasts. In addition, government entities can use the findings of this study to establish strategic plans.",

keywords = "Artificial neural networks (ANN), Autoregressive integrated moving average (ARIMA), COemissions, Forecasting, Holt-winters exponential smoothing (H-W)",

author = "Teg Alam and Ali Alarjani",

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Alam, T & Alarjani, A 2021, Forecasting CO₂Emissions in Saudi Arabia Using Artificial Neural Network, Holt-Winters Exponential Smoothing, and Autoregressive Integrated Moving Average Models. in ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings. ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 125-129, 3rd International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2021, Yogyakarta, Indonesia, 29/09/21. https://doi.org/10.1109/ICT-PEP53949.2021.9601031

Forecasting CO₂Emissions in Saudi Arabia Using Artificial Neural Network, Holt-Winters Exponential Smoothing, and Autoregressive Integrated Moving Average Models. / Alam, Teg ; Alarjani, Ali.
ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 125-129 (ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Alam, Teg

AU - Alarjani, Ali

PY - 2021

Y1 - 2021

N2 - Forecasting models are critical tools for achieving economic development and policy-making in a country. The main goal of this study is to forecast CO2 emissions in the kingdom of Saudi Arabia. In this study, Saudi Arabia's CO2 emissions are predicted using models of the Artificial Neural Network (ANN), Holt-Winters Exponential Smoothing (H-W), and Autoregressive Integrated Moving Average (ARIMA). This research uses statistical software to forecast time series data using ANN, H-W, and ARIMA models on the Kingdom of Saudi Arabia's CO2 emissions from 1960 to 2014. In addition, this study shows the forecast model accuracy using various accuracy measures. The ARIMA (2,1,2) model is found to be suitable for predicting the CO2 emissions of the Kingdom of Saudi Arabia. This study also aims to clarify the current state of CO2 emissions. This study will assist the researcher in better understanding CO2 emission forecasts. In addition, government entities can use the findings of this study to establish strategic plans.

AB - Forecasting models are critical tools for achieving economic development and policy-making in a country. The main goal of this study is to forecast CO2 emissions in the kingdom of Saudi Arabia. In this study, Saudi Arabia's CO2 emissions are predicted using models of the Artificial Neural Network (ANN), Holt-Winters Exponential Smoothing (H-W), and Autoregressive Integrated Moving Average (ARIMA). This research uses statistical software to forecast time series data using ANN, H-W, and ARIMA models on the Kingdom of Saudi Arabia's CO2 emissions from 1960 to 2014. In addition, this study shows the forecast model accuracy using various accuracy measures. The ARIMA (2,1,2) model is found to be suitable for predicting the CO2 emissions of the Kingdom of Saudi Arabia. This study also aims to clarify the current state of CO2 emissions. This study will assist the researcher in better understanding CO2 emission forecasts. In addition, government entities can use the findings of this study to establish strategic plans.

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KW - COemissions

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DO - 10.1109/ICT-PEP53949.2021.9601031

M3 - Conference contribution

AN - SCOPUS:85123465221

T3 - ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings

SP - 125

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PB - Institute of Electrical and Electronics Engineers Inc.

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Alam T , Alarjani A. Forecasting CO₂Emissions in Saudi Arabia Using Artificial Neural Network, Holt-Winters Exponential Smoothing, and Autoregressive Integrated Moving Average Models. In ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 125-129. (ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power: Emerging Energy Sustainability, Smart Grid, and Microgrid Technologies for Future Power System, Proceedings). doi: 10.1109/ICT-PEP53949.2021.9601031