Machine Learning in Estimating CO₂ Emissions from Electricity Generation

Abstract

In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models

“…ML approaches and techniques have been used for the modeling, design, and prediction of energy systems, including the use of support vector machines to estimate carbon dioxide emissions from combined cycle gas turbine plants. 30 Huang et al 31 explored the use of deep learning algorithms for air pollutant emission estimation. Tu et al 32 presented a hybrid modeling approach, named "cluster-based validated emission recalculation," for predicting the emissions of greenhouse gases from the Great Toronto Area.…”

“…Fan and Xu implemented deep learning models for predicting the health risks of occupational exposure to toxic chemicals in coal mine workplaces. ML approaches and techniques have been used for the modeling, design, and prediction of energy systems, including the use of support vector machines to estimate carbon dioxide emissions from combined cycle gas turbine plants . Huang et al explored the use of deep learning algorithms for air pollutant emission estimation.…”