Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study

arXiv cs.AI · 18 hours ago

Researchers developed Cluster-based Sequential Feature Selection (CSFS), a wrapper-based method for automatically selecting relevant input variables in wind and solar power prediction models. The method achieved comparable predictive performance to standard sequential feature selection while reducing computational cost by an average of 21%. This approach addresses the lack of systematic feature selection methods in renewable energy forecasting by providing an efficient, model-agnostic tool available as open-source software.