Evaluation of Key Performance Indicators (KPIs) for Enhancing Efficiency, Sustainability, and Operational Optimization in Renewable Energy Management using Artificial Intelligence and Large Language Models

Abstract

The integration of Large Language Models (LLMs) within renewable energy systems presents an innovative approach to optimizing energy efficiency, enhancing sustainability, and improving operational performance (Bai, J., Wang, Y., Chen, Y., et. al. 2021). Despite their potential, a clear methodology for evaluating the success of LLM implementations remains underdeveloped. This paper introduces a structured framework for evaluating Key Performance Indicators (KPIs) tailored to LLM applications in the renewable energy sector. The framework systematically addresses the assessment of LLM-driven improvements in energy forecasting accuracy, grid management, predictive maintenance, and resource optimization (Dasgupta, I., Lampinen, A. K., et. al. 2022). Critical KPIs include reductions in energy consumption during LLM training and inference, the accuracy of energy demand predictions, the optimization of renewable energy resource utilization, and the minimization of carbon footprints (Piantadosi, S. 2023). By establishing this framework, the paper provides a robust tool for measuring the impact of LLM technologies on both operational efficiency and sustainability outcomes. The study’s findings offer valuable insights for policymakers, researchers, and industry stakeholders to guide the responsible and effective integration of AI-driven solutions in renewable energy infrastructures.