Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
2026-07-15 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors looked at how people predict energy from wind turbines and solar panels, which is tricky because these depend on changing weather conditions. They found that while many data points are available, few methods exist to pick the most useful ones efficiently. To fix this, they created a new method called CSFS that automatically and effectively chooses important features for prediction models. Their tests show CSFS works as well as popular methods but is faster and less costly to use. They also shared their code to help others try it out.
renewable energywind turbine power curvephotovoltaic power predictionfeature selectionwrapper methodssequential feature selection (SFS)filter methodsRandom Forestcluster-based methodspredictive modeling
Authors
Daniel Grillmeyer, Marius Hadry, Michael Stenger, Vanessa Borst, Veronika Lesch, Samuel Kounev
Abstract
With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow. Unlike conventional power generation, the output of renewable energy sources cannot be controlled as consistently due to their dependence on environmental conditions. Therefore, reliable prediction of current and future energy production is essential. In this paper, we report findings from two structured literature reviews on real-world renewable energy prediction tasks: wind turbine power curve modeling and photovoltaic power prediction. For the former, we conducted a comprehensive literature review ourselves, while for the latter, we synthesize the key findings regarding frequently selected input features based on an existing survey. Across both domains, our analysis reveals that despite the large number of available monitoring and environmental variables, only limited or unsystematic methods for feature selection exist. To address this gap, we propose Cluster-based Sequential Feature Selection (CSFS), a novel, model-agnostic, clustering-based wrapper method for automatic, efficient, and reliable feature selection in renewable energy prediction pipelines. To support reproducibility and reuse, we provide an open-source implementation of CSFS on GitHub. We empirically evaluate the proposed approach on both use cases and compare it with established feature selection techniques such as wrapper-based sequential feature selection (SFS), filter-based methods, and Random Forest's embedded feature importance. The results show that the wrapper-based methods overall provide better-performing selections of features. CSFS achieves a predictive performance comparable to SFS while reducing computational cost by an average of 21%.