A Scoping Review of Artificial Intelligence Applications for Reducing Emissions in the Energy Sector

Аннотация

Reducing emissions in the energy sector is essential for mitigating the impact of climate change. With the growing importance of environmental sustainability, Artificial Intelligence (AI) has emerged as a transformative tool in minimizing emissions across various segments of the energy sector. This scoping review explores the applications of AI in reducing emissions, focusing on renewable energy optimization, energy efficiency, carbon capture technologies, and predictive analytics for emission forecasting. By reviewing existing literature, we identify AI-driven approaches that can aid the transition to a more sustainable energy system, highlighting challenges, opportunities, and future research directions. Keywords: Artificial Intelligence, Emissions Reduction, Renewable Energy, Carbon Capture, Energy Efficiency, Machine Learning, Deep Learning, Energy Management, Predictive Analytics, Scoping Review. patterns, and make real-time decisions. From Introduction: The global energy sector is one of the renewable energy systems to grid management, AI largest contributors to greenhouse gas emissions, techniques such as optimization algorithms, predictive particularly due to the extensive use of fossil fuels for models, and advanced sensors have been used to electricity generation, industrial processes, and enhance energy efficiency and mitigate emissions. The transportation. With the escalating concerns about purpose of this review is to provide a comprehensive climate change, reducing emissions in the energy overview of how AI can contribute to reducing sector has become a priority for governments, emissions and to identify gaps in the literature that industries, and research communities alike. Several require further exploration. strategies have been employed to reduce emissions, such as transitioning to renewable energy, improving Related Work energy efficiency, and implementing carbon capture Several studies have focused on the role of AI in and storage technologies. reducing emissions, with particular emphasis on energy Artificial Intelligence (AI) has shown significant promise system optimization, smart grid management, carbon in optimizing these strategies by providing data-driven capture, and predictive emissions modeling. Research solutions that improve operational efficiency, facilitate has shown that AI-powered solutions can significantly predictive maintenance, and enhance system design. AI improve energy efficiency in buildings, transportation, algorithms, particularly machine learning (ML) and and industrial operations by adjusting energy usage deep learning (DL), enable energy systems to predict patterns and optimizing resource allocation. and manage energy demand, optimize grid integration Renewable Energy Integration: AI algorithms, of renewable resources, and forecast emissions in real- especially machine learning models, are frequently time. This scoping review aims to examine the current used for optimizing the integration of renewable applications of AI in reducing emissions across different energy sources such as wind and solar power. These segments of the energy sector and discuss the sources are intermittent, meaning they can fluctuate in challenges and opportunities for future AI integration. output based on weather conditions. AI models can The growing trend toward AI adoption is based on its forecast renewable energy generation, predict energy ability to process large datasets, recognize complex storage needs, and balance supply and demand within American Journal of Applied Science and Technology 1 https://theusajournals.com/index.php/ajast American Journal of Applied Science and Technology (ISSN: 2771-2745) the grid. Data from the selected studies were extracted and categorized according to their application area (e.g., Energy Efficiency: AI is also utilized for energy renewable energy, energy efficiency, carbon capture, management in buildings, factories, and industrial etc.). The effectiveness of AI in each area, as well as the processes. Through real-time data collection and challenges and future prospects, were summarized and analytics, AI systems can optimize energy consumption analyzed. by adjusting settings for heating, ventilation, air conditioning (HVAC), lighting, and machinery. By RESULTS automating these functions based on data, AI reduces The scoping review identified a broad range of AI wasted energy, which directly impacts emissions. applications in the energy sector, with significant Carbon Capture and Storage (CCS): AI has been applied contributions to renewable energy optimization, to optimize the process of carbon capture and storage energy efficiency, and emission forecasting. Below, we (CCS). By integrating real-time monitoring, AI systems outline the key findings in each area. can predict emissions from industrial processes and 1. Renewable Energy Optimization optimize the efficiency of CCS technologies. This AI models, particularly machine learning (ML) includes p