• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
G. Caldarini, S. Jaf, and K. McGarry, “A Literature Survey of Recent Advances in Chatbots,” Information, vol. 13, no. 1, pp. 1-20, 2022. DOI: 10.3390/info1301004URL
2 
Omitaomu. O. A, and Haoran Niu, “Artificial Intelligence Techniques in Smart Grid: A Survey,” Smart Cities, vol. 4, no. 2, pp. 548-568, 2021.DOI:10.3390/smartcities402002DOI
3 
Alhamrouni, Ibrahim, N. Hidayah, et al., “A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions,” Applied Sciences, vol. 14, no. 14, 6214. 2024. DOI:10.3390/app1414621DOI
4 
Tianzhi. H, F. Jazizadeh, and L. Arpan, “AI-powered virtual assistants nudging occupants for energy saving: proactive smart speakers for HVAC control,” Building Research & Information, vol. 50, no. 4, pp. 394-409, 2021DOI
5 
A. K. Nayak, and S. K. Sinha, “Emerging Role of AI in Renewable Energy Sector and Smart Grid Applications,” in Proceedings of the 3rd International Conference on Artificial Intelligence and Applications (ICAIA), Springer, Singapore, pp. 365-374, 2024URL
6 
Han, Mengjie, et al., “Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts,” Buildings, vol. 14, no. 2, 371, 2024. DOI:10.3390/buildings1402037DOI
7 
X. Xu, and Y. Yang, “AI Technologies and Their Applications in Small-Scale Electric Power Systems,” IEEE Access, vol. 8, pp. 13456-13467, 2020.DOI:10.1109/ACCESS.2020.2991234URL
8 
G. R. Gunnam, D. Inupakutika, et al., “Assessing Performance of Cloud-Based Heterogeneous Chatbot Systems and A Case Study,” in IEEE Access, vol. 12, pp. 81631-81645, 2024DOI
9 
D. Khurana, A. Koli, K. Khatter, S. Singh, “Natural language processing: state of the art, current trends and challenges,” Multimedia Tools and Applications, vol. 82, pp. 3713–3744, 2023. DOI:10.1007/s11042-022-13428-4DOI
10 
A. Vaswani, N. Shazeer, et al., “Attention is All You Need,” Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, pp. 6000-6010, Dec. 2017URL
11 
T. B. Brown, B. Mann, et al., “Language Models are Few-Shot Learners,” arXiv preprint arXiv:2005.14165, 2020URL
12 
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, USA, pp. 4171-4186, 2019URL
13 
C. Raffel, N. Shazeer, et al., “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,” arXiv preprint arXiv:1910.10683, 2019URL
14 
Y. Liu, M. Ott, et al., “RoBERTa: A Robustly Optimized BERT Pretraining Approach,” arXiv preprint arXiv:1907.11692, 2019URL
15 
OpenAI, San Francisco, CA, USA. [Online]. Available: https://beta.openai.com/docs/URL
16 
Meta AI, “Llama 3 Model Documentation,” Meta AI, 2023. [Online]. Available:https://llama.meta.com/llama3/?trk=public_post-texURL
17 
Korea Electric Power Corporation, Technical Standards for Distributed Generation Interconnection with Distribution Systems, KEPCO Standard, 2020URL
18 
P. Lewis, E. Perez, A. Piktus et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” Advances in Neural Information Processing Systems, vol. 33, pp. 9459-9474, 2020.URL
19 
E. Neumayer, and T. Plümper, Robustness Tests for Quantitative Research. Cambridge, UK: Cambridge University Press, pp. 67-82, 2017URL
20 
K. Wolter, A. Avritzer, M. Vieira and A. van Moorsel, Resilience Assessment and Evaluation of Computing Systems, Springer, pp. 323-339, 2012DOI