ENHANCING UTILITY OPERATIONS THROUGH HUMAN-AI COLLABORATION
Keywords:
Human-AI Collaboration, Predictive Analyts, Predictive Analytics, Smart Energy Management, Ethical AI ImplementationAbstract
This article explores the transformative impact of human-AI collaboration in the utilities sector, examining how the integration of artificial intelligence with human expertise is reshaping operational paradigms and driving innovation. It delves into the multifaceted applications of AI in utility operations, including predictive analytics for equipment failure prevention, AI-driven customer service enhancements, and advanced data management for real-time decision support. Through a comprehensive analysis of practical examples and a detailed case study of the Catapult Project, the article illustrates the tangible benefits of this collaboration in areas such as predictive maintenance, energy demand forecasting, and grid optimization. The research highlights the critical role of human oversight in ensuring ethical AI implementation, validating AI outputs, and making strategic decisions. Looking towards the future, the article discusses emerging AI trends such as edge computing and advanced machine learning models, and their potential to further revolutionize the utility landscape. It also examines the evolving relationship between human professionals and AI systems, emphasizing the need for continuous skill development and collaborative innovation. By presenting a holistic view of the current state and prospects of human-AI collaboration in utilities, this article provides valuable insights for industry professionals, policymakers, and researchers seeking to harness the full potential of AI while maintaining the irreplaceable value of human expertise in the sector.
References
S. M. Amin and B. F. Wollenberg, "Toward a smart grid: power delivery for the 21st century," IEEE Power and Energy Magazine, vol. 3, no. 5, pp. 34-41, Sept.-Oct. 2005.
R. Johnson, A. Smith, and L. Chen, "AI and Human Expertise in Utility Management: A Collaborative Framework," IEEE Transactions on Smart Grid, vol. 12, no. 3, pp. 2234-2246, May 2021.
Y. Wang, Q. Chen, T. Hong, and C. Kang, "Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges," IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3125-3148, May 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8489897
M. Marinelli et al., "Wind and Photovoltaic Large-Scale Regional Models for Hourly Production Evaluation," IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 916-923, July 2015. [Online]. Available: https://ieeexplore.ieee.org/document/7072521
A. Humayed, J. Lin, F. Li, and B. Luo, "Cyber-Physical Systems Security—A Survey," IEEE Internet of Things Journal, vol. 4, no. 6, pp. 1802-1831, Dec. 2017. [Online]. Available: https://ieeexplore.ieee.org/document/7924372
R. Parmeshwae, D. Nikolaos, and G. Liu, "Artificial Intelligence and Machine Learning in Smart Grid: Current Applications and Future Directions," IEEE Access, vol. 9, pp. 143718-143738, 2021. [Online]. Available: https://iopscience.iop.org/article/10.1088/1755-1315/510/2/022012
