Innovative AI tools in Renewable Energy Sources

• Autorzy: Jura Przemysław , Kielichowski Bartosz

Identyfikatory

DOI

10.32056/KOMAG2025.7

• Języki publikacji: EN

Abstrakty

EN

The article analyzes the role of artificial intelligence (AI) in the renewable energy sources (RES) sector, highlighting its importance in optimizing energy production, distribution, and storage processes. AI enables precise forecasting of energy production, minimizing the effects of weather instability and increasing the operational efficiency of renewable energy systems by up to 25%. AI-based tools also allow for dynamic adjustment of wind turbines and photovoltaic panels, which reduces energy losses and operating costs. An important application of AI is predictive maintenance, which reduces failures through early detection of faults. Smart grid management enables the optimal use of renewable energy sources by analyzing demand and supply and integrating different energy storage technologies. AI also supports the planning of renewable energy investments, helping to select optimal locations for wind and solar farms. However, the implementation of AI in the energy sector faces challenges, such as the need for access to large data sets, the cost of integration with existing systems, and cybersecurity issues. Despite these barriers, the future of AI in RES looks promising, especially in the context of its integration with IoT, big data and quantum technologies. With the right technological and regulatory support, AI can become a key element of the global energy transition, increasing the stability and profitability of renewables and supporting the fight against climate change.

Słowa kluczowe

EN

artificial intelligence (AI); renewable energy sources (RES); internet of things (IoT); big data; energy optimization

PL

sztuczna inteligencja AI; odnawialne źródła energii (OZE); internet rzeczy (IoT); duże zbiory danych; optymalizacja energetyczna

• Wydawca: Instytut Techniki Górniczej KOMAG
• Czasopismo: Mining Machines
• Rocznik: 2025
• Tom: vol. 43
• Strony: 69--78
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Jura Przemysław

• Foundation Institute for Economic and Social Sciences (INES): 1 Marcina Szeligiewicza Street, 40-074 Katowice, Poland

• https://orcid.org/0000-0002-7786-1592

autor

Kielichowski Bartosz

• Foundation Institute for Economic and Social Sciences (INES): 1 Marcina Szeligiewicza Street, 40-074 Katowice, Poland

• https://orcid.org/0009-0006-8282-7811bartosz.kielichowski%40gmail.com

Bibliografia

• [1] Rashid P.:"Present and Future of AI in Renewable Energy Domain: A Comprehensive Survey",Cornell University, 2024.
• [2] Bassey K.:"Solar energy forecasting with deep learning technique",Engineering Science & Technology Journal, 2023.
• [3] Zhao E.: "New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight", Data Science and Management, 2022.
• [4] Hossain M.: "AI-Driven Optimization and Management of Decentralized Renewable Energy Grids", Nanotechnology Perceptions, 2024.
• [5] Yousef H.: "Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions",Energies, 2023.
• [6] Ahmad Hamdan K.: "AI in renewable energy: A review of predictive maintenance and energy optimization", International Journal of Science and Research Archive, 2024.
• [7] Veena S.:"Artificially intelligent models for the site-specific performance of wind turbines", International Journal of Energy and Environmental Engineering, 2020.
• [8] Dellos J. T. and Palconit C.: "Artificial Intelligence (AI) in Renewable Energy Systems: A Condensed Review of its Applications and Techniques",International Conference on Environment and Electrical Engineering (EEEIC), 2021.
• [9] Onwusinkwue S.:"Sustainable energy solutions through AI and software engineering: Optimizing resource management in renewable energy systems",Education, 2022.
• [10] Odunaiya O.: "Artificial intelligence (AI) in renewable energy: A review of predictive maintenance and energy optimization", World Journal of Advanced Research and Reviews, 2024.
• [11] Nzubechukwu Chukwudum Ohalete A.: "AI-driven solutions in renewable energy: A review of data science applications in solar and wind energy optimization", World Journal of Advanced Research and Reviews, 2023.
• [12] Lakshmi M.: "Advances in Novel Power Generation Technology and AI Software Tools", 2024.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).