LLM Agent Based Renewable Energy Forecasting Using Edge and IoT Data A Review of Solar Wind Weather and Grid Aware Decision Support
arXiv:2605.25141v1 Announce Type: new Abstract: Reliable forecasting of renewable energy generation is a foundational requirement for grid stability energy trading battery scheduling and carbon aware operational planning Solar and wind resources are inherently intermittent their output fluctuates with cloud cover wind speed atmospheric turbulence seasonal patterns and local terrain The proliferation of IoT and edge devices spanning smart meters inverters anemometers pyranometers weather stations and grid interface sensors has created an unprecedented volume of real time operational data that c
The proliferation of IoT and edge devices is now generating unprecedented volumes of real-time operational data, making advanced AI-driven forecasting for renewable energy both possible and necessary.
Reliable renewable energy forecasting is foundational for grid stability, efficient energy trading, and carbon-aware operations, directly impacting energy security and climate goals.
The capacity to accurately predict intermittent renewable energy output, significantly improving grid management, optimizing resource allocation, and accelerating the transition to sustainable energy systems.
- · Renewable energy producers
- · Grid operators
- · AI developers
- · Energy traders
- · Traditional fossil fuel generators
- · Inefficient grid management systems
Improved stability and efficiency of renewable energy integration into national grids.
Reduced reliance on fossil fuel 'peaker' plants due to better management of intermittent renewables.
Accelerated investment in renewable energy infrastructure and advanced grid technologies due to increased reliability and profitability.
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