MLOps Pipelines for GenAI in Renewable Energy: Enhancing Environmental Efficiency and Innovation

This article explores the integration of MLOps pipelines with Generative Artificial Intelligence (GenAI) in renewable energy systems, aiming to enhance environmental efficiency and foster innovation. The objectives are to evaluate advancements in energy harvesting technologies for wireless sensor networks (WSNs), analyze the potential of GenAI for optimizing renewable energy operations, and address challenges in deploying MLOps frameworks in dynamic energy environments. The principal findings reveal that MLOps pipelines enable continuous model refinement, scalability, and efficient management of GenAI models, significantly improving renewable energy applications such as resource optimization, predictive maintenance, and energy storage. Energy harvesting technologies, coupled with GenAI, promise autonomous and sustainable solutions, reducing dependency on traditional power sources. Policy implications emphasize the need for standardized regulations, investments in computational infrastructure, and ethical guidelines for AI deployment in energy systems. By addressing current challenges, policymakers and researchers can unlock GenAI's full potential, advancing global sustainability goals.