# solaR: Solar Radiation and Photovoltaic Systems with R

## Introduction

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.

## Software

## Documentation

The best place to learn how to use the package is the companion paper published by the Journal of Statistical Software: http://www.jstatsoft.org/v50/i09/

This book (in Spanish) contains detailed information about solar
radiation and photovoltaic systems. In my articles I
frequently use `solaR`

. Besides, I publish news and examples
about `solaR`

at this blog.

## Citation

If you use `solaR`

, please cite it in any publication reporting
results obtained with this software:

Oscar Perpiñán (2012). solaR: Solar Radiation and Photovoltaic Systems with R, Journal of Statistical Software, 50(9), 1-32. URL http://www.jstatsoft.org/v50/i09/.

A BibTeX entry for LaTeX users is

@Article{, title = {{solaR}: Solar Radiation and Photovoltaic Systems with {R}}, author = {Oscar Perpi{\~n}{\'a}n}, journal = {Journal of Statistical Software}, year = {2012}, volume = {50}, number = {9}, pages = {1--32}, url = {http://www.jstatsoft.org/v50/i09/}, }

## References

`solaR`

has been cited in these publications:

- Ward, H. C., Kotthaus, S., Grimmond, C. S. B., Bjorkegren, A., Wilkinson, M., Morrison, W. T. J., Evans, J.G. Morison, J.I.L., Iamarino, M. (2015). Effects of urban density on carbon dioxide exchanges: Observations of dense urban, suburban and woodland areas of southern England. Environmental Pollution, 198, 186–200. 10.1016/j.envpol.2014.12.031
- Pebesma, E., Bivand, R., & Ribeiro, P. J. (2015). Software for Spatial Statistics. Journal of Statistical Software, 63(1). Retrieved from http://www.jstatsoft.org/v63/i01
- Abdulla, K., & Wirth, Andrew Halgamuge, Saman K Steer, K. C. (2014). Selecting an Optimal Combination of Storage & Transmission Assets with a Non-Dispatchable Electricity Supply. In 7th International Conference on Information and Automation for Sustainability (ICIAfS). Retrieved from https://edas.info/cd/iciafs14/papers/1570042835.pdf
- Antonanzas, J., Jimenez, E., Blanco, J., & Antonanzas-Torres, F. (2014). Potential solar thermal integration in Spanish combined cycle gas turbines. Renewable and Sustainable Energy Reviews, 37, 36–46. 10.1016/j.rser.2014.05.006
- Davila-Gomez, L., Colmenar-Santos, A., Tawfik, M., & Castro-Gil, M. (2014). An accurate model for simulating energetic behavior of PV grid connected inverters. Simulation Modelling Practice and Theory, 49, 57–72. 10.1016/j.simpat.2014.08.001
- Gutierrez Corea, F. V. (2014, December 1). Predicción espacio-temporal de la irradiancia solar global a corto plazo en España mediante geoestadística y redes neuronales artificiales. E.T.S.I. en Topografía, Geodesia y Cartografía (UPM). Retrieved from http://oa.upm.es/34145/1/Federico_Vladimir_Gutierrez_Corea.pdf
- Gutierrez-Corea, F.-V., Manso-Callejo, M.-A., Moreno-Regidor, M.-P., & Velasco-Gómez, J. (2014). Spatial estimation of sub-hour Global Horizontal Irradiance based on official observations and remote sensors. Sensors (Basel, Switzerland), 14(4), 6758–87. 10.3390/s140406758
- Rhodes, J. D., Upshaw, C. R., Cole, W. J., Holcomb, C. L., & Webber, M. E. (2014). A multi-objective assessment of the effect of solar PV array orientation and tilt on energy production and system economics. Solar Energy, 108, 28–40. 10.1016/j.solener.2014.06.032
- Schmidt, J., Cancella, R., & Junior, A. O. P. (2014). An optimal mix of solar PV, wind and hydro power for a low-carbon electricity supply in Brazil. Retrieved from ftp://ftp.boku.ac.at/pub/repecftpg/repecftp/RePEc/sed/wpaper/572014.pdf
- Ploennigs, J., Chen, B., Schumann, A., & Brady, N. (2013). Exploiting Generalized Additive Models for Diagnosing Abnormal Energy Use in Buildings. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings (pp. 17:1–17:8). New York, NY, USA: ACM. 10.1145/2528282.2528291
- Street, M., Reinhart, C., Norford, L., & Ochsendorf, J. (2013). Urban heat island in Boston–an evaluation of urban air-temperature models for predicting building energy use. In Proceedings of BS2013: 13th Conference of International Building Per-formance Simulation Association, August (pp. 26–28).
- Ummel, K. (2011). SEXPOT: A spatiotemporal linear programming model to simulate global deployment of renewable power technologies. Central European University.