Macroscopic and Energy-Based Greenhouse Gas Emissions Predictions: Current Techniques and Future Directions

EasyChair Preprint 15003

Abstract

Predicting greenhouse gas emissions is a crucial effort in mitigating climate change and reducing the harmful effects of these gases. Various machine learning models have been employed for intelligent prediction of greenhouse gas emissions, both at a macroscopic level and through energy demand forecasting. The most popular models include Long Short Term Memory (LSTM), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Random Forest (RF). To enhance the performance of these models, numerous optimization techniques have been utilized, with those from the swarm intelligence group being particularly prominent. Current research challenges involve selecting the appropriate machine learning model and optimization technique, addressing dependency on official data, overcoming model interpretability limitations, and dealing with training data constraints. Future research opportunities lie in discovering or modifying existing machine learning models and optimization techniques, utilizing transfer learning to mitigate limited training data issues, and leveraging quantum computing-based optimization techniques to refine existing machine learning models.

Keyphrases: Sustainability, greenhouse gas emission, machine learning

@booklet{EasyChair:15003,
  author    = {Wahyu Hidayat and Nur Ulfa Maulidevi and Kridanto Surendro},
  title     = {Macroscopic and Energy-Based Greenhouse Gas Emissions Predictions: Current Techniques and Future Directions},
  howpublished = {EasyChair Preprint 15003},
  year      = {EasyChair, 2024}}