Operational Wind Speed Forecasts for Chile's Electric Power Sector Using a Hybrid ML Model

Abstract

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.

Figures (4)

• Figure 1: Mean hourly RMSE and Normalized RMSE of wind magnitude predictions averaged across all interpolated wind farm locations for 2021. (a) Short-term forecasts (0-2 days). The left column represents mean hourly wind magnitude RMSE values over a 48-hour forecast horizon, while the right column represents normalized RMSE considering HRES as the baseline using TiDE forecasts at 6-hour intervals. (b) Medium-term forecasts (2-10 days). The left column represents wind magnitude RMSE values over an 8 day forecast horizon at 6-hour time intervals. The solid lines represent three alternate GraphCast models.
• Figure 2: Mean hourly generation and standard deviation for wind, solar, and thermal power plants in Chile from 2019 to 2023. Each row corresponds to a different year, and each column represents a different technology (Wind, Solar, and Thermal). The solid lines indicate the mean generation in gigawatt-hours (GWh), and the shaded areas represent the standard deviation across all hours. The x-axis denotes the hour of the day, ranging from 0 to 23. This figure illustrates the temporal variation in power generation for each technology over the years, highlighting both the average generation and the variability within each day.
• Figure 3: Hourly solar and wind generation (2019-2023). Solar (left) and Wind (right). The colored bars represent mean generation normalized by installed capacity by source and year. The bars indicate the standard deviation of hourly normalized generation by source.
• Figure 4: Normalized RMSE difference of GraphCast's 10u forecasts relative to HRES, by location, at 12 hours, 2 days and 10 day lead times. Blue indicates that GraphCast has greater skill than HRES, Red that HRES has greater skill. Here "10u" refers to the u-component of wind at an altiitude corresponding to 10m lam2022graphcast