Application of extreme learning machine (ELM) forecasting model on CO2 emission dataset of a natural gas-fired power plant in Dhaka, Bangladesh

• Published in: Data in brief Vol. 54; p. 110491
• Main Authors: Rahman, Mustafizur, Rashid, Faijunnesa, Roy, Sujit Kumar, Habib, Md. Ahosan
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.06.2024; Elsevier
• Subjects: Air pollutants; Application of ELM model; Bangladesh; carbon dioxide; case studies; climate change; CO2 emission from natural gas-fired power plant; Data; data collection; electricity generation; Emission forecasting; natural gas; power plants; prediction; Bangladesh; Air pollutants; Application of ELM model; CO2 emission from natural gas-fired power plant; Emission forecasting
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2024.110491

Understanding and predicting CO2 emissions from individual power plants is crucial for developing effective mitigation strategies. This study analyzes and forecasts CO2 emissions from an engine-based natural gas-fired power plant in Dhaka Export Processing Zone (DEPZ), Bangladesh. This study also presents a rich dataset and ELM-based prediction model for a natural gas-fired plant in Bangladesh. Utilizing a rich dataset of Electricity generation and Gas Consumption, CO2 emissions in tons are estimated based on the measured energy use, and the ELM models were trained on CO2 emissions data from January 2015 to December 2022 and used to forecast CO2 emissions until December 2026. This study aims to improve the understanding and prediction of CO2 emissions from natural gas-fired power plants. While the specific operational strategy of the studied plant is not available, the provided data can serve as a valuable baseline or benchmark for comparison with similar facilities and the development of future research on optimizing operations and CO2 mitigation strategies. The Extreme Learning Machine (ELM) modeling method was employed due to its efficiency and accuracy in prediction. The ELM models achieved performance metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE), values respectively 3494.46 (<5000), 2013.42 (<2500), and 0.93 close to 1, which falls within the acceptable range. Although natural gas is a cleaner alternative, emission reduction remains essential. This data-driven approach using a Bangladeshi case study provides a replicable framework for optimizing plant operations and measuring and forecasting CO2 emissions from similar facilities, contributing to global climate change.