Informatics & Modeling

ARTIFICIAL INTELLIGENCE–BASED SYSTEMS FOR CLIMATE CHANGE MODELING AND PREDICTION

Komal Bamugade and Archana Jadhav

This review systematically examines AI (ML, deep learning, etc.) applications in climate change modeling and prediction, with emphasis on improving carbon footprint accuracy and climate pattern understanding. It identifies challenges like data quality and interpretability,…

via Zenodo (CERN European Organization for Nuclear Research)

Development of a Forecasting Framework Based on Advanced Machine Learning Algorithms for Greenhouse Gas Emissions

Ene Yalçın S.

This paper proposes a forecasting framework for greenhouse gas emissions using advanced machine learning algorithms. The title suggests a methodological contribution to enhance emission prediction, though specific methods and results are unavailable without the abstract.

via Systems

Artificial Intelligence, Energy and Climate Change

Chris Meniw

This whitepaper analyzes the dual role of AI: optimizing power grids and integrating renewables while increasing energy and carbon footprint from compute infrastructure. It examines deployments in smart grids, industrial optimization, and regional climate modeling, quantifying…

via Zenodo (CERN European Organization for Nuclear Research)

AI-optimized renewable energy forecasting for U.S. power grids

Ishmael Jesse Narh Adikorley, Eunice Abena Lettu

This study explores AI and machine learning techniques for forecasting renewable energy generation and integrating it into smart grids. By combining traditional time-series methods with ML, forecasting accuracy improves, enabling higher reliance on renewables. The findings…

via Magna Scientia Advanced Research and Reviews

OPTIMIZATION OF RENEWABLE ENERGY SYSTEMS USING MACHINE LEARNING ALGORITHMS

Wilson, R. T.

This review evaluates machine learning optimization of renewable energy systems, focusing on solar/wind forecasting, storage optimization, and grid integration. It highlights improved efficiency, reliability, and sustainability, serving as a practical reference for engineers.

via Zenodo

Leveraging artificial intelligence for optimizing renewable energy systems and enhancing climate sustainability

Abdullahi Umar Nasiru, Binibor Mary-Ann Ekomerenren, Abdul Salam Abdul Fattah, …

This study explores AI applications for optimizing renewable energy systems in Nigeria. Machine learning and predictive analytics improve demand forecasting, grid stability, and maintenance efficiency, reducing carbon emissions. Challenges include limited data infrastructure and…

via World Journal of Advanced Engineering Technology and Sciences

A machine learning framework for residential district cooling: Forecasting consumption, explaining drivers, and evaluating decarbonization pathways

Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, …

This paper proposes a machine learning framework for forecasting consumption, explaining drivers, and evaluating decarbonization pathways in residential district cooling systems. It aims to improve energy efficiency and reduce CO2 emissions.

via Energy Reports

AI-Optimized Sensor Network and Signal Processing Model for Advanced Manufacturing and Green Energy Applications

Sarvaree Bano, Priyanka Gupta

This study proposes an AI-optimized sensor network and signal processing model to enhance efficiency and sustainability in advanced manufacturing and green energy systems. It applies AI for predictive maintenance and real-time optimization of renewable energy plants,…

via International Conference Intelligent Computing and Control Systems

Trustworthy Data-Driven Hybrid Modeling of Building Energy Performance and Greenhouse Gas Emissions

Abdulkadir Gungor, Ahmet Nur, Sabir Rustemli, …

This study develops a hybrid data-driven framework combining machine learning and emission factor rescaling to predict campus-wide CO2 emissions. An Artificial Neural Network achieved the best predictive performance (RMSE=2.13 ton/year, R²=0.985). Feature importance analysis…

via Buildings

Internet of Things and Artificial Intelligence for Carbon Emissions Monitoring and Forecasting A Systematic Review of Smart Environmental Accounting Systems

Tsitsi Shannon Chaparika, Monika Gondo

This systematic review of 100 peer-reviewed studies demonstrates that IoT sensor networks and AI models effectively support real-time carbon monitoring and emissions forecasting. However, the integration with carbon accounting models remains weak, highlighting the need for…

Greenhouse gas emissions

Stephan Krinke, Yu-Yi Lin

A study on greenhouse gas emissions, likely covering sources, trends, and mitigation strategies critical for decarbonization.

via Elgar Encyclopedia of Life Cycle Sustainability Assessment

Energy companies' carbon reduction, low-carbon transformation, and green innovation for deep learning algorithms under the carbon neutrality goal

Xiaohui Xie

This paper proposes a carbon emission modeling framework based on graph neural networks. It constructs a heterogeneous graph with production equipment, energy consumption units, and emission factors as nodes, using multi-scale graph convolution and dynamic attention for key node…

via Journal of Renewable and Sustainable Energy

Energy-Aware Responsible AI for Climate Action

Rudra Anand Swant, Tanmay Jitendra Tambe, Ayush Rajkumar Munot, …

Explores AI's dual role in climate action: as a tool for mitigation/adaptation and as an environmental burden. Proposes an energy-aware responsible AI framework integrating technical efficiency, environmental responsibility, policy alignment, and Global South engagement.

via Advances in computational intelligence and robotics book series

Harnessing Artificial Intelligence, Machine Learning, and Drone Technologies for Climate Change Monitoring and Mitigation

Dr. Mala. C. Patil, Dr. A. M. Nageswara Yogi

This paper explores the synergistic use of AI, ML, and drones for climate change monitoring and mitigation. Drones provide high-resolution real-time data from hard-to-reach areas, which AI/ML processes to detect patterns, predict changes, and improve climate models. Applications…

via Open MIND

Environmental Science and Engineering

Rohit Das

This paper comprehensively reviews AI-powered optimization in renewable energy grids, covering forecasting, grid management, predictive maintenance, and energy trading. It presents case studies demonstrating tangible benefits, while critically examining challenges such as data…

A Review on Smart Grid Optimization Using Artificial Intelligence and Machine Learning

Charvi Goel, Mamta Rani, Rakhi Kamra

This comprehensive review analyzes the application of AI and machine learning in smart grid optimization, essential for transitioning to sustainable energy systems. It covers key areas including load forecasting, stability assessment, fault detection, and cybersecurity, and…

via Zenodo

Machine learning in energy storage optimization for carbon neutrality: A review

P. Balakrishnan

This review comprehensively examines machine learning applications in optimizing energy storage systems for carbon neutrality. It categorizes methods that improve battery operation efficiency and renewable energy integration, and outlines future research directions.

via Renewable Energy

Machine learning-based hybrid dynamic modeling and economic predictive control of carbon capture process for ship decarbonization

Xuewen Zhang, Kuniadi Wandy Huang, Dat-Nguyen Vo, …

This paper proposes a machine learning-based hybrid dynamic model and economic predictive control for carbon capture processes on ships. It aims to improve CO2 capture efficiency and reduce operational costs, contributing to decarbonization of the shipping sector.

via Chemical Engineering Science