arXiv AI By Daniel Grillmeyer, Marius Hadry, Michael Stenger, Vanessa Borst, Veronika Lesch, Samuel Kounev

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

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arXiv:2607. 14024v1 Announce Type: cross 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.

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arXiv Machine Learning
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Energy Yield and Lifetime Climate Classification via Machine Learning for Optimizing Photovoltaic Module Design and Materials

The paper presents a machine‑learning based climate classification tailored for photovoltaic (PV) modules, incorporating both energy yield and module lifetime with climate‑dependent degradation. Using an interpolated dataset of twelve input features, the authors identify annual global horizontal irradiation and ambient temperature as the most influential predictors, achieving RMSEs of 0.007 MWh for yield and 1.5 years for lifetime. The resulting hierarchical clustering yields six primary climate clusters (Tropical, Desert, Continental, Temperate, Boreal, Polar) and 15 subclusters, with the low‑temperature continental climate delivering the highest discounted lifetime energy yield.

By Youri Blom, Sofia Dutto, Alexandru Costache, Rowan Richie, Ruben Pelsser, Wesley Berger, Jing Sun, Rudi Santbergen, Olindo Isabella, Malte Ruben Vogt
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WPBench: A Comprehensive Benchmark for Wind Power Forecasting

arXiv:2609.24444v1 Announce Type: new Abstract: Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market o...

By Yuhan Zhu, Jilin Hu, Xinying Cai, Yingshan Li, Li Ma, Xiangfei Qiu Linsen Li, Kai Zhang, Yao Fu, Weihao Jiang, Bin Yang