Harnessing Generative AI for Optimizing Power Generation Innovations and Applications in Energy Efficiency
Keywords:
generative artificial intelligence, energy efficiency, renewable energy sources, energy system optimization, failure prediction, smart grids, digital twinsAbstract
This article explores modern approaches to the integration of advanced technologies such as generative artificial intelligence (AI), the Internet of Things (IoT), and 5G, aimed at developing digital infrastructure and strategic partnerships with technology companies in the energy sector. The focus is on methods such as the use of generative AI models for big data analysis, failure prediction, and the optimization of energy grid operational processes. Special attention is given to the integration of IoT and 5G to create a flexible and resilient infrastructure capable of adapting to real-time changes. The key conclusions from this work show that these technologies not only reduce operational costs but also significantly enhance environmental sustainability through the integration of renewable energy sources. Furthermore, the analysis indicates that the implementation of Vehicle-to-Grid systems contributes to more efficient energy management, and when combined with IoT and Phasor Measurement Units (PMUs), improves the monitoring and control of electrical networks. The article emphasizes that despite the need for adaptation of existing infrastructure and significant computational resources, the potential of these technologies will continue to grow, offering innovative solutions for reducing energy consumption and enhancing productivity in the long term.
References
. Boopathi S. Achievements in the optimization of intelligent energy systems through the integration of intelligent networks, machine learning and the Internet of Things //Methods of optimization of hybrid energy systems: Renewable energy sources, electric vehicles and smart grids. – IGI Global, 2024. – pp. 33-61.
. Baash G., Russo G., Evins R. Conditional generating competitive network for energy use in several buildings using limited data // Energetics and artificial intelligence. – 2021. – Vol. 5. – p. 100087.
. Shan R. Navigating uncharted waters: New technologies and future challenges in the field of generative AI using Python // Advanced applications of generative AI. – 2024. – pp. 61-84.
. Magd X. and others . Artificial Intelligence is the driving force of Industry 4.0 //A roadmap for the implementation of industry 4.0 using artificial intelligence. - 2022. – pp. 1-15.
. Feyerrigel S. et al. Generative Artificial Intelligence //Development of business and information systems. – 2024. – vol. 66. – No. 1. – pp. 111-126.
. Al-Shahri O. A. et al. Methods of optimization of solar photovoltaic energy, challenges and problems: a comprehensive review //Journal of Environmentally friendly Production. – 2021. – vol. 284. – p. 125465.
. Rain N. The contribution of ChatGPT and other types of generative artificial intelligence (AI) to renewable and sustainable energy //Available at: SSRN 4597674. – 2023.
. Hafiz G. et al. An innovative optimization strategy for effective energy consumption management with a demand response signal for the day ahead and forecasting energy consumption in a smart grid using an artificial neural network // IEEe Access. – 2020. – Vol. 8. – pp. 84415-84433.
. Chen S. et al. Artificial intelligence in the economic assessment of energy efficiency and renewable energy technologies //Technologies and assessments of sustainable energy. – 2021. – vol. 47. – p. 101358.
. Abrahamsen F. E., Ai Yu., Cheffena M. Communication technologies for intelligent networks: a comprehensive review //Sensors. – 2021. – vol. 21. – no. 23. – p. 8087.
. Quitzow L., Rode F. Imagine a smart city using smart grids? The future of urban energy between technological experiments and an imaginary low-carbon city //Urban studies. – 2022. – vol. 59. – No. 2. – pp. 341-359.
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