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Renewable energies are becoming increasingly vital for electrical grid stability as conventional plants are being displaced, reducing their role in redispatch interventions. To incorporate Wind Power (WP) in redispatch planning, day-ahead forecasts are required to assess availability. Automated, scalable forecasting models are necessary for deployment across thousands of onshore WP turbines. However, irregular redispatch shutdowns complicate WP forecasting, as autoregressive methods use past generation data. This paper analyzes state-of-the-art forecasting methods with both regular and irregular shutdowns. Specifically, it compares three autoregressive Deep Learning (DL) methods with WP curve modeling, finding the latter has lower forecasting errors, fewer data cleaning requirements, and higher computational efficiency, suggesting its advantages for practical use.

On autoregressive deep learning models for day-ahead wind power forecasts with irregular shutdowns due to redispatching