Bias correction of wind power forecasts with SCADA data and continuous learning

arXiv:2402.13916v1 Announce Type: new
Abstract: Wind energy plays a critical role in the transition towards renewable energy sources. However, the uncertainty and variability of wind can impede its full potential and the necessary growth of wind power capacity. To mitigate these challenges, wind power forecasting methods are employed for applications in power management, energy trading, or maintenance scheduling. In this work, we present, evaluate, and compare four machine learning-based wind power forecasting models. Our models correct and improve 48-hour forecasts …

abstract arxiv bias capacity challenges continuous cs.lg data energy forecasting growth power renewable role transition type uncertainty wind