Multi-Agent AI Chief of Staff for Turbine Fleet Operations
Integrating Advanced Wind Speed Forecasting to Optimise Real Time Performance, Maintenance and Grid Integration
DOI:
https://doi.org/10.63665/IJAICE.0203.01Keywords:
Multi Agent Systems, Wind Speed Forecasting, Turbine Fleet Operations, Predictive Maintenance, Grid Integration, Retrieval Augmented Generation, Human in the Loop, Orchestration, SCADAAbstract
Modern wind turbine fleets generate an overwhelming volume of telemetry, weather, maintenance and market signals, and the operators who must act on that volume are increasingly stretched thin. This paper proposes a multi agent artificial intelligence system, framed as an AI chief of staff, that coordinates six specialised agents, a forecasting agent, a SCADA monitoring agent, a predictive maintenance agent, a grid compliance and trading agent, a reporting and human approval agent, and an orchestrator that plans and routes work between them. The system integrates a hybrid CNN LSTM attention forecasting model for wind speed and power prediction, a shared context bus for short term coordination, a vector store for retrieval augmented generation over manuals and grid codes, and a human in the loop approval gate before any command reaches the physical fleet. We describe the business context, stakeholders, problem statement and objectives that motivate the design, present the agent architecture and interaction flow, and provide a full, runnable Python reference implementation in the appendix. Simulated experiments across an eight turbine fleet show that the proposed forecasting model reduces mean absolute error from 1.92 metres per second under a persistence baseline to 0.71 metres per second, and that the composite multi agent system improves curtailment loss reduction, maintenance downtime reduction and grid penalty reduction relative to a rule based baseline. The results suggest that multi agent orchestration, when combined with structured outputs and human oversight, is a practical path towards safer and more efficient autonomous operation of renewable energy fleets.
References
[1] T. Ackermann, Wind Power in Power Systems, John Wiley and Sons, 2012.
[2] S. Hochreiter and J. Schmidhuber, Long Short Term Memory, Neural Computation, vol. 9, no. 8, pp. 1735 to 1780, 1997.
[3] A. Vaswani et al., Attention Is All You Need, Advances in Neural Information Processing Systems, 2017.
[4] S. J. Taylor and B. Letham, Forecasting at Scale, The American Statistician, vol. 72, no. 1, pp. 37 to 45, 2018.
[5] G. E. P. Box, G. M. Jenkins, G. C. Reinsel and G. M. Ljung, Time Series Analysis, Forecasting and Control, John Wiley and Sons, 2015.
[6] M. Wooldridge, An Introduction to MultiAgent Systems, John Wiley and Sons, 2009.
[7] P. Lewis et al., Retrieval Augmented Generation for Knowledge Intensive NLP Tasks, Advances in Neural Information Processing Systems, 2020.
[8] Z. Hameed, Y. S. Hong, Y. M. Cho, S. H. Ahn and C. K. Song, Condition Monitoring and Fault Detection of Wind Turbines and Related Algorithms, A Review, Renewable and Sustainable Energy Reviews, vol. 13, no. 1, pp. 1 to 39, 2009.
[9] International Electrotechnical Commission, IEC 61400 series, Wind Turbines.
[10] National Energy System Operator (NESO), Grid Code, Great Britain Transmission System Requirements. NESO assumed this role from National Grid Electricity System Operator on 1 October 2024.
[11] S. M. Valdivia Bautista, J. A. Dominguez Navarro, M. Perez Cisneros, C. J. Vega Gomez and B. Castillo Tellez, Artificial Intelligence in Wind Speed Forecasting, A Review, Energies, vol. 16, no. 5, article 2457, 2023.
[12] R. K. Pandit, D. Astolfi and I. Durazo Cardenas, A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines, Energies, vol. 16, no. 4, article 1654, 2023.
[13] S. Russell and P. Norvig, Artificial Intelligence, A Modern Approach, Pearson, 2020.
[14] Y. Shoham and K. Leyton Brown, Multiagent Systems, Algorithmic, Game Theoretic and Logical Foundations, Cambridge University Press, 2008.
[15] J. F. Manwell, J. G. McGowan and A. L. Rogers, Wind Energy Explained, Theory, Design and Application, John Wiley and Sons, 2010.
[16] OpenAI, Agents SDK, Handoffs, Guardrails and Tracing, Developer Documentation, developers.openai.com, accessed 2026.
[17] OpenAI, Migrate to the Responses API, Developer Documentation, platform.openai.com, 2025. The Assistants API was deprecated on 26 August 2025 with a sunset date of 26 August 2026.
[18] S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan and Y. Cao, ReAct, Synergizing Reasoning and Acting in Language Models, International Conference on Learning Representations, 2023.
[19] T. Schick, J. Dwivedi Yu, R. Dessi, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda and T. Scialom, Toolformer, Language Models Can Teach Themselves to Use Tools, Advances in Neural Information Processing Systems, 2023.
[20] J. S. Park, J. O'Brien, C. J. Cai, M. R. Morris, P. Liang and M. S. Bernstein, Generative Agents, Interactive Simulacra of Human Behavior, Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, 2023.
[21] X. S. Si, W. Wang, C. H. Hu and D. H. Zhou, Remaining Useful Life Estimation, A Review on the Statistical Data Driven Approaches, European Journal of Operational Research, vol. 213, no. 1, pp. 1 to 14, 2011.
[22] P. Tchakoua, R. Wamkeue, M. Ouhrouche, F. Slaoui Hasnaoui, T. A. Tameghe and G. Ekemb, Wind Turbine Condition Monitoring, State of the Art Review, New Trends, and Future Challenges, Energies, vol. 7, no. 4, pp. 2595 to 2630, 2014.
[23] P. Fleming, J. Annoni, J. J. Shah, L. Wang, S. Ananthan, Z. Zhang, K. Hutchings, P. Wang, W. Chen and L. Chen, Field Test of Wake Steering at an Offshore Wind Farm, Wind Energy Science, vol. 2, no. 1, pp. 229 to 239, 2017.
[24] M. Lydia, S. S. Kumar, A. I. Selvakumar and G. E. P. Kumar, A Comprehensive Review on Wind Turbine Power Curve Modeling Techniques, Renewable and Sustainable Energy Reviews, vol. 30, pp. 452 to 460, 2014.
[25] T. Burton, N. Jenkins, D. Sharpe and E. Bossanyi, Wind Energy Handbook, Second Edition, John Wiley and Sons, 2011.
[26] A. Krizhevsky, I. Sutskever and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, 2012.
[27] J. Devlin, M. W. Chang, K. Lee and K. Toutanova, BERT, Pre Training of Deep Bidirectional Transformers for Language Understanding, Proceedings of the North American Chapter of the Association for Computational Linguistics, 2019.
[28] T. Brown et al., Language Models Are Few Shot Learners, Advances in Neural Information Processing Systems, 2020.
[29] I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016.
[30] D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman and D. Mane, Concrete Problems in AI Safety, arXiv preprint arXiv, 1606.06565, 2016.
[31] F. Doshi Velez and B. Kim, Towards A Rigorous Science of Interpretable Machine Learning, arXiv preprint arXiv, 1702.08608, 2017.
[32] P. Stone and M. Veloso, Multiagent Systems, A Survey from a Machine Learning Perspective, Autonomous Robots, vol. 8, no. 3, pp. 345 to 383, 2000.
[33] B. Hayes Roth, A Blackboard Architecture for Control, Artificial Intelligence, vol. 26, no. 3, pp. 251 to 321, 1985.
[34] Anthropic, Building Effective Agents, Anthropic Research, December 2024.
[35] A. Ng, Agentic Design Patterns, Reflection, Tool Use, Planning and Multi Agent Collaboration, The Batch, DeepLearning.AI, March 2024.
[36] N. Shinn, F. Cassano, E. Berman, A. Gopinath, K. Narasimhan and S. Yao, Reflexion, Language Agents with Verbal Reinforcement Learning, Advances in Neural Information Processing Systems, 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Author
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
