Safe and Explainable Renewable Energy Forecasting using Retrieval-Augmented Generation and Guardrails

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Forecasting is essential for ensuring grid stability given the unpredictable nature of renewables like solar power and wind energy. Deep learning methods that have been deployed to date can generate inaccurate forecasts in certain situations and fail to satisfy physical assumptions and constraints. In this paper, we propose a framework to enhance the accuracy and reliability of renewable energy forecast models. The RAG framework is augmented with safety guardrails to ensure that the forecasts are robust, accurate, and interpretable. Specifically, the framework retrieves historical weather-generation patterns relevant to the current state to increase the context-awareness of the time series models. We further deploy the guardrails to constrain predictions and avoid impossible values. Through experiments done on benchmark data from the National Renewable Energy Laboratory and Global Energy Forecasting Competition, we prove that our approach reduces prediction errors and constraint violations compared to the baselines.


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  • Topic: Safe and Explainable Renewable Energy Forecasting using Retrieval-Augmented Generation and Guardrails
    Format: Virtual webinar
    Date: Thursday, June 18, 2026 (third Thursday of June)
     
  • Starts 30 May 2026 04:42 PM UTC
  • Ends 18 June 2026 11:42 PM UTC
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Rakesh More

Topic:

Safe and Explainable Renewable Energy Forecasting using Retrieval-Augmented Generation and Guardrails

 

Short Bio (100–150 words)
Senior Manager – AI and Finance Portfolio & Application Management A.J. Gallagher |Dec 2021 – Present
  •  Lead enterprise AI portfolio strategy integrating machine learning and advanced analytics across insurance business functions.
  •   Direct large-scale AI and cloud transformation programs supporting risk assessment, fraud detection, and financial analytics platforms.
  •   Implement responsible AI governance frameworks aligned with regulatory standards, including NAIC and data privacy requirements.
  •   Manage cross-functional teams delivering enterprise AI platforms across multiple business units.

Biography:

PROFESSIONAL SUMMARY

  •  AI and machine learning leader with 20+ years of experience delivering enterprise-scale intelligent systems in the insurance and financial services sectors.
  •  Specializing in responsible AI, generative AI governance, and large-scale AI transformation programs across underwriting, claims, fraud detection, and risk analytics.
  •  Active researcher in generative AI safety with work focusing on integration of Retrieval-Augmented Generation (RAG) architectures with guardrails frameworks to improve trustworthiness and reliability of large language models in enterprise environments.

KEY CONTRIBUTIONS

  • Led enterprise AI strategy initiatives integrating machine learning and generative AI across insurance operations, including underwriting, claims, and fraud detection. 
  • Conducted research on the integration of Retrieval-Augmented Generation (RAG) with guardrails frameworks to mitigate hallucinations in enterprise LLM systems. 
  • Developed evaluation approaches for LLM reliability, hallucination reduction, and responsible AI deployment in regulated industries. 
  • Led cross-functional AI initiatives involving data science, engineering, legal, and compliance teams to deploy responsible AI frameworks aligned with regulatory requirements.

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Agenda

Renewable Energy Forecasting, Retrieval-Augmented Generation, Explainable AI, Smart Grid, Guardrails, Time-Series Prediction, AI Safety