Estimation of solar radiation using stepwise multiple linear regression with principal component analysis in Algeria

Abstract

For the modelling of solar systems, reliable and complete time series of solar radiation are required. However, solar radiation data from ground measurements or satellite images may be available only for limited time periods and often have data quality issues or/and data gaps. It is possible to get reasonably accurate radiation estimates of solar radiation from meteorological parameters, which can complement or extend existing data. In this paper, a model based on eight meteorological parameters (evaporation, temperature, wind speed, visibility, cloud cover, sunshine duration, sunshine ratio and global solar radiation) is developed to predict the global solar radiation using 29-year (1986–2014) data from Oran radiometric station in Algeria. Two versions of the multiple regression analysis are used: the first with original observation variables (called as manifest variables) and the second by PCs called as latent variables or (common) factors. Separate analyses have been carried out for two scenarios: overall and partial study. The first scenario uses all climatic dataset, but the last scenario uses three subsets which are sunny, partly cloudy and cloudy days. This classification has been obtained using certain statistical properties that we have considered as thresholds. Stepwise multiple linear regression analysis is used to fit global solar radiation data using meteorological variables as predictors. A variable selection method based on PCA technique are used to obtain the subsets of predictors to be included in the regression model of global solar radiation data. Hence, the proposed relationships may be considered useful for predicting global solar radiation rate in other sites of Algeria that have climatic conditions similar to the study area.