README.md

In frimane/SolarClusGnr: Solar irradiance time-series clustering and downscaling

SolarClusGnr: Solar irradiance time-series clustering and down-scaling

SolarClusGnr

It is an R-package allows a reproducible research for non-parametric clustering and downscaling of daily solar irradiation time-series. The current version includes:

• SIR_Data Constructor function of objects of SIRData class. Once the user creates a SIRData object from his data, he no longer need other inputs, ALL the rest work will done automatically.

• DPGMMclus S3 Method for non-parametric Bayesian Dirichlet-Gaussian mixture model clustering of daily clearness index distributions. It can be also used to perform any data clustering of class matrix other than irradiance data. It generate an object of class clusData containing the clustering outputs.

• parClusGen Constructor function of objects of genData class, needed for the generation of hight resolution solar irradiance data.

• GenData Function to generate high resolution solar irradiance time-series. It requires object of genData class as input.

• clPlot Function to generate plots of the resulting classes.

Further inquiry

The author is happy to answer your questions and is open to future collaborations on this topic. Please contact: [e-mail] or [e-mail].

Installation

Users can install the development version of SolarClusGnr:

with either the remotes package:

remotes::install_github("frimane/SolarClusGnr")

or with the devtools package:

devtools::install_github("frimane/SolarClusGnr")

Citation

The original paper describing the methods implemented in SolarClusGnr is:

Frimane, Â., Soubdhan, T., Bright, J.M., Aggour, M., 2019. Nonparametric bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data. Solar Energy 182, 462-479. URL:http://www.sciencedirect.com/science/article/pii/S0038092X19301781, doi:https://doi.org/10.1016/j.solener.2019.02.052. 

The BibTex entry:

@article{Frimane2019,
title = "Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data",
journal = "Solar Energy",
volume = "182",
pages = "462-479",
year = "2019",
issn = "0038-092X",
doi = "https://doi.org/10.1016/j.solener.2019.02.052",
url = "http://www.sciencedirect.com/science/article/pii/S0038092X19301781",
author = "{\^A}zeddine Frimane and Ted Soubdhan and Jamie M. Bright and Mohammed Aggour",
keywords = "Solar irradiance, Clustering, Clearness index, Bayesian nonparametric, Synthetic irradiance",
} 

License

This package is free and open source software under MIT-license.

frimane/SolarClusGnr documentation built on May 8, 2019, 8:58 p.m.