Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
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 pre
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- PossiblePossibly related (embedding) · 50%Quantifying drivers of photovoltaic power generation at Bhadla using explainable machine learning and causal discovery - Nature →
- LinkedLinked via arxiv author · 85%Daniel Grillmeyer →
“Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study”
- LinkedLinked via arxiv author · 85%Marius Hadry →
“Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study”
- LinkedLinked via arxiv author · 85%Michael Stenger →
“Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study”
- LinkedLinked via arxiv author · 85%Vanessa Borst →
“Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study”
- LinkedLinked via arxiv author · 85%Veronika Lesch →
“Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study”
- LinkedLinked via arxiv author · 85%Samuel Kounev →
“Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study”