Title :
Multivariate regression for prediction of solar irradiance
Author :
Nalina, U. ; Prema, V. ; Smitha, K. ; Rao, K. Uma
Author_Institution :
Dept. of Electr. & Electron. Eng., RV Coll. of Eng., Bangalore, India
Abstract :
This paper describes regression models to forecast solar irradiance for a short term (or period). The regression models enable the prediction of solar irradiance in minute values over a period of a few days. A single variate regression model is used and various plots obtained between solar irradiance as dependent variable and air temperature and relative humidity as independent variables have been studied. Optimal range for prediction using regression is decided. To obtain accuracy multivariate regression is carried out It also presents new multifunctional relationship between solar irradiance, air temperature and relative humidity. This multifunctional regression relationship gives more accurate results compared to other methods having single variable. In this regression model solar irradiance follows an increasing trend upto a particular temperature after which it shows decreasing trend and hence it has been modeled with three equations.
Keywords :
humidity; regression analysis; solar power; sunlight; air temperature; multifunctional relationship; multivariate regression; regression models; relative humidity; single variate regression model; solar irradiance prediction; Atmospheric modeling; Correlation; Equations; Humidity; Input variables; Mathematical model; Temperature distribution; Solar irradiance; prediction; regression; relative humidity;
Conference_Titel :
Data Science & Engineering (ICDSE), 2014 International Conference on
Conference_Location :
Kochi
Print_ISBN :
978-1-4799-6870-1
DOI :
10.1109/ICDSE.2014.6974633