%0 Journal Article %1 Zhang2014Statistical %A Zhang, Xiongwen %D 2014 %J Renewable Energy %K energy statistics solar renewables %P 307--314 %R 10.1016/j.renene.2014.05.038 %T A statistical approach for sub-hourly solar radiation reconstruction %U http://dx.doi.org/10.1016/j.renene.2014.05.038 %V 71 %X Highlights • A new methodology was proposed to forecast the sub-hourly solar radiation. • The Gaussian kernel density estimator is employed to construct the model. • A case study is presented for solar radiation reconstruction in Singapore. Abstract This paper proposes a computational-statistics based approach for solar radiation reconstruction at sub-hourly intervals. A dimensionless form of stochastic variable, V, which is defined as the difference between the theoretical global solar radiation in clear-sky conditions and the actual solar radiation, normalized by the clear-sky global solar radiation, is introduced and adopted in this work. The probability density function of V is calculated from historical data using a Gaussian kernel density estimator. With the developed model, the only input information required for the reconstruction procedure is the cloud condition of the sky (i.e., fair, partly cloudy, overcast, and rain/snow etc.). A case study in simulating solar radiation in Singapore is conducted to validate the accuracy of the model. The calculated results agree well with the measured data. The normalized root mean square error (NRMSE) is on average 23.4\% and 7.2\% for the one-minute temporal resolution and hourly integral values, respectively.

@article{Zhang2014Statistical, abstract = {Highlights • A new methodology was proposed to forecast the sub-hourly solar radiation. • The Gaussian kernel density estimator is employed to construct the model. • A case study is presented for solar radiation reconstruction in Singapore. Abstract This paper proposes a computational-statistics based approach for solar radiation reconstruction at sub-hourly intervals. A dimensionless form of stochastic variable, V, which is defined as the difference between the theoretical global solar radiation in clear-sky conditions and the actual solar radiation, normalized by the clear-sky global solar radiation, is introduced and adopted in this work. The probability density function of V is calculated from historical data using a Gaussian kernel density estimator. With the developed model, the only input information required for the reconstruction procedure is the cloud condition of the sky (i.e., fair, partly cloudy, overcast, and rain/snow etc.). A case study in simulating solar radiation in Singapore is conducted to validate the accuracy of the model. The calculated results agree well with the measured data. The normalized root mean square error (NRMSE) is on average 23.4\% and 7.2\% for the one-minute temporal resolution and hourly integral values, respectively.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Zhang, Xiongwen}, biburl = {https://www.bibsonomy.org/bibtex/2418ef972f521eda4811bdc9c87351f5e/pbett}, citeulike-article-id = {13248059}, citeulike-attachment-1 = {Zhang2014.pdf; /pdf/user/pbett/article/13248059/974124/Zhang2014.pdf; ab7eb8a14dbe533f585878c4286d1f0be02a637d}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.renene.2014.05.038}, comment = {(private-note)Got from library}, doi = {10.1016/j.renene.2014.05.038}, file = {Zhang2014.pdf}, interhash = {27f4733030cbb9d87484f9d05f4c989b}, intrahash = {418ef972f521eda4811bdc9c87351f5e}, issn = {09601481}, journal = {Renewable Energy}, keywords = {energy statistics solar renewables}, month = nov, pages = {307--314}, posted-at = {2014-07-02 10:16:08}, priority = {2}, timestamp = {2018-06-22T18:34:20.000+0200}, title = {A statistical approach for sub-hourly solar radiation reconstruction}, url = {http://dx.doi.org/10.1016/j.renene.2014.05.038}, volume = 71, year = 2014 }