Zapisane w:
| Główni autorzy: | , |
|---|---|
| Format: | Recurso digital |
| Język: | angielski |
| Wydane: |
Zenodo
2025
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| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.5281/zenodo.17570101 |
| Etykiety: |
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Spis treści:
- <p>This dataset supports the study <em>“Optimizing Monthly Solar PV Tilt Angles and Energy Yield Across Global Climate Zones: A Hybrid Machine Learning and PVLib Approach.”</em> It provides simulation-derived optimal monthly tilt angles and machine learning–ready input features for photovoltaic (PV) performance optimization across 17 globally distributed cities representing tropical, subtropical, temperate, and cold/subpolar climate regions.</p> <p>The dataset combines:</p> <ul> <li> <p><strong>Typical Meteorological Year (TMY)</strong> climate data from PVGIS</p> </li> <li> <p><strong>PV energy output simulations</strong> using PVLib (tilt range 0°–70°)</p> </li> <li> <p><strong>Derived optimal monthly tilt angles</strong> based on maximum simulated energy yield</p> </li> <li> <p><strong>Machine learning features</strong>, including irradiance components, atmospheric variables, geographic coordinates, and one-hot encoded climate zone identifiers</p> </li> </ul> <p>The dataset enables:</p> <ul> <li> <p>Monthly and seasonal tilt optimization for rooftop or ground-mounted PV systems</p> </li> <li> <p>Climate-sensitive PV yield analysis across regions and latitudes</p> </li> <li> <p>Training and benchmarking of supervised ML models for tilt prediction</p> </li> <li> <p>Comparative study of empirical, seasonal, and ML-derived tilt strategies</p> </li> </ul> <p>The file <code>all_cities_ml_ready_onehot.csv</code> contains ~67,000 samples. Each row represents a unique city-month observation with associated environmental variables, simulation-derived energy outputs for multiple tilt angles, and the corresponding optimal tilt label. This facilitates both regression (predict tilt angle) and classification (tilt category) tasks.</p> <p>This dataset is released under a <strong>Creative Commons Attribution 4.0 (CC BY 4.0)</strong> license to support transparency and reproducibility in solar PV research. Users may share, adapt, and build upon the dataset with proper citation.</p>