TY - JOUR
T1 - Hybrid beta-KDE model for solar irradiance probability density estimation
AU - Wahbah, Maisam
AU - El-Fouly, Tarek H.M.
AU - Zahawi, Bashar
AU - Feng, Samuel
N1 - Funding Information:
Manuscript received June 11, 2018; revised September 23, 2018, November 27, 2018, and February 12, 2019; accepted April 9, 2019. Date of publication April 22, 2019; date of current version March 23, 2020. This work was supported by the Advanced Power and Energy Center, Khalifa University, UAE. Paper no. PESL-00126-2018. (Corresponding author: Tarek Hussein M. EL-Fouly.) The authors are with the Khalifa University, Abu Dhabi 127788, UAE (e-mail: [email protected]; [email protected]; bashar.zahawi@ kustar.ac.ae; [email protected]).
Publisher Copyright:
© 2010-2012 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - This letter proposes a hybrid Beta-kernel density estimation (KDE) model for solar irradiance probability density estimation. The model combines parametric and nonparametric approaches to avoid KDE boundary bias and obtain a more reliable statistical model of solar irradiance. The performance of the hybrid model is assessed via comparisons with the Beta distribution and two KDE models that employ different bandwidth selection methods. The assessment is carried out using the Kolmogorov-Smirnov goodness-of-fit test, and four measures of error: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE). Results confirm the accuracy of the hybrid model for solar irradiance modeling with percentage improvements over the Beta distribution of up to 13.8% (RMSE), 11.7% (MAE), 19.3% (MAPE), and 72.5% (MBE). The K-S test results show that the proposed Beta-KDE hybrid is the only model for which the null hypothesis is not rejected for any of the eight datasets considered in this study.
AB - This letter proposes a hybrid Beta-kernel density estimation (KDE) model for solar irradiance probability density estimation. The model combines parametric and nonparametric approaches to avoid KDE boundary bias and obtain a more reliable statistical model of solar irradiance. The performance of the hybrid model is assessed via comparisons with the Beta distribution and two KDE models that employ different bandwidth selection methods. The assessment is carried out using the Kolmogorov-Smirnov goodness-of-fit test, and four measures of error: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE). Results confirm the accuracy of the hybrid model for solar irradiance modeling with percentage improvements over the Beta distribution of up to 13.8% (RMSE), 11.7% (MAE), 19.3% (MAPE), and 72.5% (MBE). The K-S test results show that the proposed Beta-KDE hybrid is the only model for which the null hypothesis is not rejected for any of the eight datasets considered in this study.
KW - Kernel density estimation
KW - parametric statistics
KW - solar irradiance models
KW - statistical analysis
UR - http://www.scopus.com/inward/record.url?scp=85082583834&partnerID=8YFLogxK
U2 - 10.1109/TSTE.2019.2912706
DO - 10.1109/TSTE.2019.2912706
M3 - Article
AN - SCOPUS:85082583834
SN - 1949-3029
VL - 11
SP - 1110
EP - 1113
JO - IEEE Transactions on Sustainable Energy
JF - IEEE Transactions on Sustainable Energy
IS - 2
M1 - 8695145
ER -