AI's Role in Sustainable Energy
DOI:
https://doi.org/10.61841/cg5pet16Keywords:
Artificial Intelligence (AI), Renewable Energy,, IoT, Energy storage, Space ExplorationAbstract
This study overlook on the perspective role of AI in the energy sector, with main focus on improving efficiency and sustainability. The role is to establish a realistic benchmark for AI technology, facilitating global efforts, ambitions, novel applications, and industry obstacles. The study focuses three main areas:
- The application of AI in solar and hydrogen power
- The utilisation of AI for contribution and demand management control of energy
- An overview of the latest techniques in AI technology
This research explores how AI techniques outperform traditional models across various domains, including controllability, energy efficiency optimization, cyber-attack prevention, IoT (Internet of Things), big data handling, smart grid management, robotics, predictive maintenance control and computational efficiency. The findings of this study underscore AI as a pivotal tool for the evolving and data intensive energy industry, empowering enhanced operational performance and efficiency within an increasingly competitive landscape. This research overlooks how AI techniques outperform traditional models across various domains, including
Controllability, energy efficiency optimising, cyber-attack precaution, IoT (Internet of Things), big data handling, smart grid managing, robotics, predictive maintenance control and computational efficiency. The findings of this study underscore AI as an important tool for the evolving and data intensive energy industry, empowering enhanced operational performance and efficiency within a rapid competitive landscap
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References
1. Chakraborty, I., & Bandyopadhyay, S. (2020). Artificial intelligence and machine learning for renewable energy- based power generation systems: A review. Sustainable Energy Technologies and Assessments,
2. Gao, Y., Song, X., & Li, P. (2020). Artificial intelligence for renewable energy generation and consumption: A review. Renewable and Sustainable Energy Reviews, 117, 109499.
Singh, M. K., & Srivastava, S. (2019). Artificial intelligence and machine learning in renewable energy systems: A state-of-the-art review. Renewable and Sustainable Energy Reviews, 101, 596-611.
3. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115.
4. Zou, Y., & Zhang, W. (2019). A review of artificial intelligence technologies for energy management in smart grid systems. IEEE Access, 7, 41182-41196.
Yang, Z., & Hong, W. C. (2019). Applications of artificial intelligence in power systems. IEEE Transactions on Smart Grid, 10(4), 3631-3643.
5. Samadi, A., Mohammadi-ivatloo, B., Abbaspour, M., & Sheikh-El-Eslami, M. K. (2020). Artificial intelligence applications in energy systems operation and planning: State of the art and future trends. Energy,
6. Mohsenian-Rad, A. H., Wong, V. W., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010). Autonomous demand- side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 1(3), 320-331.
7. Brown, G., Reardon, T., Ho, G., & Bialek, J. (2020). An artificial intelligence approach to forecasting electricity demand with extreme temporal granularity. IEEE Transactions on Power Systems, 35(3), 1740-1748.
8. Mohsenian-Rad, A. H., & Leon-Garcia, A. (2009). Optimal residential load control with price prediction in real- time electricity pricing environments. IEEE Transactions on Smart Grid, 1(2), 120-133.
9. Du, L., & Aazami, A. (2018). Artificial intelligence in smart grid: A review. IEEE Access, 6, 2667-2678
10. G. Kumar and R. Sharma, "Analysis of software reliability growth model under two types of fault and warranty cost," 2017 2nd International Conference on System Reliability and Safety (ICSRS), Milan, Italy, 2017, Kumar,
11. G., Kaushik, M. and Purohit, R. (2018) “Reliability analysis of software with three types of errors and imperfect debugging using Markov model,” International journal of computer applications in technology, 58(3),
12. Sharma, R. and Kumar, G. (2017) “Availability improvement for the successive K-out-of-N machining system using standby with multiple working vacations,” International journal of reliability
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