Operational Wind Speed Forecasts for Chile's Electric Power Sector Using a Hybrid ML Model
This work addresses the problem of grid stability and emissions reduction for Chile's electric power sector by improving wind speed forecasts, though it is incremental as it combines existing ML models for a specific domain.
The paper tackles the challenge of accurately forecasting wind speed for Chile's electric power sector to manage grid operations amid increasing renewable energy integration, and introduces a hybrid ML model that outperforms existing operational deterministic systems by 4-21% for short-term and 5-23% for medium-term forecasts.
As Chile's electric power sector advances toward a future powered by renewable energy, accurate forecasting of renewable generation is essential for managing grid operations. The integration of renewable energy sources is particularly challenging due to the operational difficulties of managing their power generation, which is highly variable compared to fossil fuel sources, delaying the availability of clean energy. To mitigate this, we quantify the impact of increasing intermittent generation from wind and solar on thermal power plants in Chile and introduce a hybrid wind speed forecasting methodology which combines two custom ML models for Chile. The first model is based on TiDE, an MLP-based ML model for short-term forecasts, and the second is based on a graph neural network, GraphCast, for medium-term forecasts up to 10 days. Our hybrid approach outperforms the most accurate operational deterministic systems by 4-21% for short-term forecasts and 5-23% for medium-term forecasts and can directly lower the impact of wind generation on thermal ramping, curtailment, and system-level emissions in Chile.