Operational Wind Speed Forecasts for Chile's Electric Power Sector Using a Hybrid ML Model
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph Neural NetworkMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Calibration of wind speed ensemble forecasts for power generation
In the last decades wind power became the second largest energy source in the EU covering 16% of its electricity demand. However, due to its volatility, accurate short range wind power predictions are required for succes…
BIG-bench Machine LearningMachine learning-based probabilistic forecasting of solar irradiance in Chile
By the end of 2023, renewable sources cover 63.4% of the total electric power demand of Chile, and in line with the global trend, photovoltaic (PV) power shows the most dynamic increase. Although Chile's Atacama Desert i…
Weather ForecastingA two-step machine learning approach to statistical post-processing of weather forecasts for power generation
By the end of 2021, the renewable energy share of the global electricity capacity reached 38.3% and the new installations are dominated by wind and solar energy, showing global increases of 12.7% and 18.5%, respectively.…
Improving solar wind forecasting using Data Assimilation
Data Assimilation (DA) has enabled huge improvements in the skill of terrestrial operational weather forecasting. In this study, we use a variational DA scheme with a computationally efficient solar wind model and in sit…
Weather ForecastingSelf-attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts
Current postprocessing techniques often require separate models for each lead time and disregard possible inter-ensemble relationships by either correcting each member separately or by employing distributional approaches…