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

2026-07-15

AI Summary

This study addresses the challenge that renewable energy generation is highly sensitive to environmental factors, and existing forecasting methods often lack efficient, systematic feature selection mechanisms, thereby limiting model performance. To overcome this limitation, the authors propose a model-agnostic Cluster-based Sequential Feature Selection (CSFS) method, which employs clustering as a preprocessing step to reduce redundant computations and integrates a wrapper strategy to maintain high prediction accuracy while substantially improving computational efficiency. Experimental results on wind and photovoltaic power forecasting tasks demonstrate that CSFS achieves prediction accuracy comparable to that of standard Sequential Forward Selection (SFS), yet reduces average computational overhead by 21%, outperforming conventional approaches such as filter methods and Random Forest–based embedded techniques. The implementation code has been made publicly available to facilitate reproducibility and practical adoption.

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%.

Problem

• renewable energy prediction
• feature selection
• wind power forecasting
• solar power forecasting
• input variable selection

Innovation

• feature selection
• renewable energy prediction
• wrapper method
• clustering
• computational efficiency