Fueling the Future: A Comprehensive Analysis and Forecast of Fuel Consumption Trends in U.S. Electricity Generation

Md Monjur Hossain Bhuiyan (), Ahmed Nazmus Sakib, Syed Ishmam Alawee and Talayeh Razzaghi
Additional contact information
Md Monjur Hossain Bhuiyan: School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA
Ahmed Nazmus Sakib: School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA
Syed Ishmam Alawee: School of Data Science and Analytics, University of Oklahoma, Norman, OK 73019, USA
Talayeh Razzaghi: School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA

Sustainability, 2024, vol. 16, issue 6, 1-30

Abstract: The U.S. Energy Information Administration (EIA) provides crucial data on monthly and annual fuel consumption for electricity generation. These data cover significant fuels, such as coal, petroleum liquids, petroleum coke, and natural gas. Fuel consumption patterns are highly dynamic and influenced by diverse factors. Understanding these fluctuations is essential for effective energy planning and decision making. This study outlines a comprehensive analysis of fuel consumption trends in electricity generation. Utilizing advanced statistical methods, including time series analysis and autocorrelation, our objective is to uncover intricate patterns and dependencies within the data. This paper aims to forecast fuel consumption trends for electricity generation using data from 2015 to 2022. Several time series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and seasonal Naïve), Seasonal and Trend Decomposition using Loess (STL), exponential smoothing (ETS), and the Autoregressive Integrated Moving Average (ARIMA) method, have been applied. The best-performing models are determined based on Root Mean Squared Error (RMSE) values. For natural gas (NG) consumption, the ETS model achieves the lowest RMSE of 20,687.46. STL demonstrates the best performance for coal consumption with an RMSE of 5936.203. The seasonal Naïve (SNaïve) model outperforms the others for petroleum coke forecasting, yielding an RMSE of 99.49. Surprisingly, the Mean method has the lowest RMSE of 287.34 for petroleum liquids, but the ARIMA model is reliable for its ability to capture complex patterns. Residual plots are analyzed to assess the models’ performance against statistical parameters. Accurate fuel consumption forecasting is very important for effective energy planning and policymaking. The findings from this study will help policymakers strategically allocate resources, plan infrastructure development, and support economic growth.

Keywords: fuel consumption; forecasting; time series analysis; sustainable energy policies; RMSE (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/6/2388/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/6/2388/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:6:p:2388-:d:1356364

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().