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solaR: Solar Radiation and Photovoltaic Systems with R

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  • Perpiñán Lamigueiro, Oscar

Abstract

The solaR package allows for reproducible research both for photovoltaics (PV) systems performance and solar radiation. It includes a set of classes, methods and functions to calculate the sun geometry and the solar radiation incident on a photovoltaic generator and to simulate the performance of several applications of the photovoltaic energy. This package performs the whole calculation procedure from both daily and intradaily global horizontal irradiation to the final productivity of grid-connected PV systems and water pumping PV systems. It is designed using a set of S4 classes whose core is a group of slots with multivariate time series. The classes share a variety of methods to access the information and several visualization methods. In addition, the package provides a tool for the visual statistical analysis of the performance of a large PV plant composed of several systems. Although solaR is primarily designed for time series associated to a location defined by its latitude/longitude values and the temperature and irradiation conditions, it can be easily combined with spatial packages for space-time analysis.

Suggested Citation

  • Perpiñán Lamigueiro, Oscar, 2012. "solaR: Solar Radiation and Photovoltaic Systems with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i09).
    • Handle: RePEc:jss:jstsof:v:050:i09
    DOI: http://hdl.handle.net/10.18637/jss.v050.i09
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    References listed on IDEAS

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    1. Zeileis, Achim & Grothendieck, Gabor, 2005. "zoo: S3 Infrastructure for Regular and Irregular Time Series," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i06).
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    1. Abdulsalam S. Alghamdi & AbuBakr S. Bahaj & Yue Wu, 2017. "Assessment of Large Scale Photovoltaic Power Generation from Carport Canopies," Energies, MDPI, vol. 10(5), pages 1-22, May.
    2. Schmidt, Johannes & Cancella, Rafael & Pereira, Amaro O., 2016. "An optimal mix of solar PV, wind and hydro power for a low-carbon electricity supply in Brazil," Renewable Energy, Elsevier, vol. 85(C), pages 137-147.
    3. Yu Wang & Mikael Boulic & Robyn Phipps & Manfred Plagmann & Chris Cunningham, 2020. "Experimental Performance of a Solar Air Collector with a Perforated Back Plate in New Zealand," Energies, MDPI, vol. 13(6), pages 1-16, March.
    4. Simon Liebermann & Jung-Sup Um & YoungSeok Hwang & Stephan Schlüter, 2021. "Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts," Energies, MDPI, vol. 14(11), pages 1-21, May.
    5. Drücke, Jaqueline & Borsche, Michael & James, Paul & Kaspar, Frank & Pfeifroth, Uwe & Ahrens, Bodo & Trentmann, Jörg, 2021. "Climatological analysis of solar and wind energy in Germany using the Grosswetterlagen classification," Renewable Energy, Elsevier, vol. 164(C), pages 1254-1266.

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