TY - GEN
T1 - Forecasting CO2Emissions in Saudi Arabia Using Artificial Neural Network, Holt-Winters Exponential Smoothing, and Autoregressive Integrated Moving Average Models
AU - Alam, Teg
AU - Alarjani, Ali
N1 - Publisher Copyright:
© 2021 IEEE.
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.
KW - Artificial neural networks (ANN)
KW - Autoregressive integrated moving average (ARIMA)
KW - COemissions
KW - Forecasting
KW - Holt-winters exponential smoothing (H-W)
UR - http://www.scopus.com/inward/record.url?scp=85123465221&partnerID=8YFLogxK
U2 - 10.1109/ICT-PEP53949.2021.9601031
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
EP - 129
BT - ICT-PEP 2021 - International Conference on Technology and Policy in Energy and Electric Power
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2021
Y2 - 29 September 2021 through 30 September 2021
ER -
