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

Abstract

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.

Keywords: Air pollutants; Application of ELM model; CO2 emission from natural gas-fired power plant; Emission forecasting.