AI in Energy: Overcoming Unforeseen Obstacles

Danish, Mir Sayed Shah

doi:10.3390/ai4020022

Cited by 14 publications

(7 citation statements)

References 60 publications

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“…Fig. 1 Historical perspective of AI & ML penetration in the energy domain [124] The application of artificial intelligence and machine learning algorithms has the potential to improve resource allocation in renewable energy projects. This can be accomplished by identifying the most suitable locations for new installations, selecting the renewable energy technologies that are most suitable for particular sites, and streamlining the distribution of resources such as land, materials, and labor [125], [126].…”

Section: Photovoltaic Thermal Systemmentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

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Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

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Section: Photovoltaic Thermal Systemmentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…The French energy industry leverages these concepts to enhance grid stability, increase energy efficiency, and accelerate the transition to a sustainable energy future [1]. AI helps solve energy management problems quickly and efficiently using predictive analytics, real-time monitoring, and adaptive grid management [13]. Predictive analytics can significantly improve the energy industry by projecting energy production and consumption trends, allowing for better demand response management [33].…”

Section: Theoretical/conceptual Frameworkmentioning

confidence: 99%

“…This intelligent technology integration represents a significant step towards more sustainable and consumer-friendly energy management in France. However, integrating AI into energy management raises considerable obstacles, including concerns about data privacy and security and the significant investments required to modify existing infrastructure to accept advanced AI technologies [13]. The inherent complexity of AI algorithms necessitates the establishment of robust legal frameworks to ensure these technologies are implemented fairly and transparently.…”

Section: Introductionmentioning

confidence: 99%

Integrating Artificial Intelligence for Enhanced Grid Stability and Renewable Energy Management in France

2024

IJFMR

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This integrative literature review (ILR) delves deeply into the role of artificial intelligence (AI) in enhancing grid stability and managing renewable energy sources in France. The central issue in this study is the difficulty in integrating intermittent renewable sources such as wind and solar electricity, which impacts the energy grid's stability and efficiency. The review looks into how artificial intelligence might improve the prediction and optimization of energy output from these volatile sources, enhancing supply- and demand management. It demonstrates AI's potential to improve grid stability, minimize waste, and promote sustainable energy practices. The study also cites essential barriers such as data protection, infrastructural sufficiency, and the substantial investments required to modernize existing systems for AI integration. Based on a thorough examination of existing research, the review underlines the need for solid legislative frameworks to support ethical AI deployment in line with France's environmental and energy goals. This research paper is critical for policymakers because it provides insights into the strategic application of AI to promote a more efficient and resilient energy industry. The study's findings and recommendations urge further AI research and practical applications to guide France and other nations to a more sustainable and stable energy future.

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“…Several scholarly articles delve into the challenges of implementing AI in clean energy technologies. "AI in Energy: Overcoming Unforeseen Obstacles" [31] offers a multidisciplinary approach, emphasizing the lack of regulation and standards for AI in energy systems and the challenge of scalability, especially when considering safety and environmental protection standards. "Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities" [7] provides a baseline for understanding AI's current status in the sustainable energy industry, touching upon challenges like data availability, algorithm selection, and the ethical and social implications.…”

Section: Challenges To Ai Implementation In Clean Energy Technologiesmentioning

confidence: 99%

“…The global impact of the COVID-19 pandemic on energy dynamics and the intertwined opportunities with AI technologies are explored in "Crises and opportunities in terms of energy and AI technologies during the COVID-19 pandemic" [34]. "AI in Energy: Overcoming Unforeseen Obstacles" [31] emphasizes the necessity of a coordinated approach, serving as a guide for policymakers, energy enterprises, and scholars. Delving into the broader implications, "How artificial intelligence will affect the future of energy and climate" [35] contemplates AI's influence on energy supply-demand and its ramifications on climate change.…”

Section: Opportunities For Ai In Clean Energy Technologymentioning

confidence: 99%

Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review

Necula

2023

Energies

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This systematic review investigates the role of artificial intelligence (AI) in advancing clean energy technologies within Europe, based on a literature survey from 2006 to 2023. The assessment reveals that AI, particularly through deep learning and neural networks, enhances the efficiency, optimization, and management of clean energy systems. Noteworthy is AI’s capacity to improve short-term energy forecasts, essential for smart cities and IoT applications. Our findings indicate that AI drives innovation in renewable energy, contributing to the development of smart grids and enabling collaborative energy-sharing models. While the research underscores AI’s substantial influence in Europe’s energy sector, it also identifies gaps, such as varied AI algorithm applications in different renewable energy sectors. The study emphasizes the need for integrating AI with emerging clean energy innovations, advocating for interdisciplinary research to navigate the socio-economic, environmental, and policy dimensions. This approach is crucial for guiding a sustainable and balanced advancement in the clean energy landscape, signifying AI’s pivotal role in Europe’s energy transition.

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AI in Energy: Overcoming Unforeseen Obstacles

Cited by 14 publications

References 60 publications

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Integrating Artificial Intelligence for Enhanced Grid Stability and Renewable Energy Management in France

Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review

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