Predicting solar radiation fluxes for solar energy system applications

Abstract

The mean daily global solar radiation flux is influenced by astronomical, climatological, geographical, geometrical, meteorological, and physical parameters. This paper deals with the study of the effects of influencing parameters on the mean daily global solar radiation flux, and also with the computation of the solar radiation flux at the surface of the earth in locations without solar radiation measurements. The reference-real data were borrowed from the Iranian Meteorological Organization. The analysis of data showed that the mean daily solar radiation flux on a horizontal surface is related to parameters such as: mean daily extraterrestrial solar radiation, average daily ratio of sunshine duration, mean daily relative humidity, mean daily maximum air temperature, mean daily maximum dew point temperature, mean daily atmospheric pressure, and sine of the solar declination angle. Multiple regression and correlation analysis were applied to predict the mean daily global solar radiation flux on a horizontal surface. The models were validated when compared with the referencemeasured data of global solar radiation flux. The results showed that the models estimate the global solar radiation flux within a narrow relative error band. The values of mean bias errors and root mean square errors were within acceptable margins. The predicted values of global solar radiation flux by this approach can be used for the design and performance estimation in solar applications. The model can be used in areas where meteorological stations do not exist and information on solar radiation flux cannot be obtained experimentally.

Models for Calculating Monthly Average Solar Radiation from Air Temperature in Swaziland

“…In addition, the performance of the models can be quantified by evaluating the root-mean-square-error (RMSE) and the mean-percentage-error (MPE). These are fundamental measures of accuracy in solar energy calculations (Saffaripour, 2013;, Muzathik, 2011Okundamiya, 2016). They are respectively defined as ( ) Where n=12 is the number of data pairs, calc j H , is the j-th calculated value and meas j H , is the j-th measured value.…”

“…However, weather stations will, at times, not have data on solar radiation because the instruments for radiation measurement, such as pyrometers and solarimeters, may not be available. As a result, mathematical models have been developed and calibrated to estimate solar radiation in different parts of the world such as in Brazil (Dos Santos, 2014), Iran (Saffaripour, 2013), India (Bajpai, 2009), Algeria/Spain (Chegaar,1998), China (Li, 2014a;2014b), Bangladesh (Datta, 2013), Chile (Meza, 2000), USA (Allen, 1997) and Nigeria (Umoh, 2013). These models estimate solar radiation as a function of meteorological parameters such as temperature, atmospheric pressure, relative humidity, the number of sunshine hours, wind speed, cloud cover, and rainfall.…”

Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest

“…Each GEFS data file consists of the forecast values of one variable, e.g. precipitation or temperature, by the each of the 11 ensemble members of NWP model on each day, at 5 different time steps of the next day (12,15,18,21,24 h), on specified latitudes and longitudes (16 Â 9 grid). As observed in Section 2.2.2, all these weather variables are strongly correlated with the target variable i.e.…”

Development of the Monthly Average Daily Solar Radiation Map Using a-Cbr, Fem, and Kriging Method

“…However, it is too expensive to measure the actual MADSR data using the remote sensors in all the sites where the PV system would be installed. To solve this problem, previous studies used various methodologies to estimate the MADSR data in the unmeasured locations (Al-Alawi, Al-Hinai 1998; Alsamamra et al 2009;Behrang et al 2010;Caglayan et al 2014;Cogliani et al 2008;Janjai 2010;Mohandes et al 1998;Mubiru, Banda 2008;Ramedani et al 2014;Saffaripour et al 2013;Şahin et al 2013;Sözen et al 2005;Šúri, Hofierka 2004). Such methodologies could be categorized in three ways: (i) artificial intelligence technique; (ii) geostatistical technique; and (iii) satellite retrieval technique.…”