arXiv:2510.15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations. As renewable penetration grows, reliable probabilistic forecasting is becoming essential for managing uncertainty and supporting risk-aware operational decision-making. However, these forecasts often suffer from miscalibration due to temporal variability, changing weather conditions, and heterogeneous operating regimes. In many real-world settings, renewable energy forecasts are provided by external sources, vendors, or indepen
The increasing penetration of renewable energy sources into power grids necessitates more reliable and accurate forecasting to manage inherent variability and support operational decision-making.
Improved AI-driven probabilistic forecasting directly addresses a critical challenge in scaling renewable energy, which is essential for grid stability, resource management, and economic efficiency.
The ability to generate more robust and calibrated renewable energy forecasts, even when data sources are external or independent, changes how grid operators and energy markets can plan and react.
- · Renewable energy producers
- · Grid operators
- · AI/ML solution providers
- · Energy traders
- · Traditional forecasting methods
- · Energy utilities with static operational models
More efficient integration of renewables into national grids, reducing curtailment and ensuring supply reliability.
Accelerated investment in renewable energy projects due to reduced operational risk and increased predictability.
Potential for new decentralized energy markets and trading strategies based on highly accurate, real-time probabilistic forecasts.
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