Comparison of solar radiation correlations for ?zmir, Turkey

Abstract

SUMMARYIn this study, empirical correlations are developed to estimate the monthly average daily global solar radiation on a horizontal surface (H) for the city of Izmir in Turkey. Experimental data were measured in the Solar}Meteorological Station of the Solar Energy Institute at Ege University. The present models are then compared with the 25 models available in the literature for calculating H based on the main percentage error, root mean error, the main bias error, and correlation coe$cient. It can be concluded that the present models predict the values of H for Izmir better than other available models.

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

“…2). The estimation capability of the monthly EDI from the two machine learning algorithms was statistically evaluated using the following score metrics or prediction error indicators: RootMean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R 2 ) (Paulescu et al, 2011;Ulgen and Hepbasli, 2002) and the Willmott's Index of Agreement (d) (Acharya et al, 2013;Willmott, 1982) viz…”

“…In Australia, the GCM framework implemented as the official model used by the Australian Bureau of Meteorology (BOM) is the Predictive Ocean Atmosphere Model for Australia (POAMA) (Hudson et al, 2011;Zhao and Hendon, 2009) and that by the Queensland Department of Environment and Resource Management is the statistical analysis of climate indices by the Seasonal Pacific Ocean Temperature Analysis (SPOTA-1) (Day et al, 2010). However the predictions of rainfall by GCMs on some occasions have failed to predict very wet or very dry conditions that produced significant economic consequences (van den Honert and McAneney, 2011). For example the floods between November 2010 and January 2011 that left three-quarters of Queensland, Australia a disaster zone (Hurst, 2011) were not predicted well in advance (Abbot and Marohasy, 2014;Inquiry, 2011;Seqwater, 2011).…”

Monthly average daily global and diffuse solar radiation based on sunshine duration and clearness index for Brasov, Romania

“…The selected models are chosen because they are the most widely used by researchers in order to estimate the monthly average daily global and diffuse horizontal solar radiation and they give very good results for different locations. 2,19,28,31,32 They are briefly presented below.…”

“…2,12,[15][16][17][18][19][20][26][27][28][29][30][31][32][33][34][35][36]57,58 These models are empirical, theoretical, or stochastic, leading to sophisticated models using neural networks and fuzzy logic algorithms. [21][22][23][24][25] The input data for such models include quantities such as the sunshine duration, the latitude and the longitude, the declination angle, the altitude, the cloudiness, the ambient temperature, the relative humidity, the atmospheric pressure, and precipitate water vapor.…”

“…[21][22][23][24][25] The input data for such models include quantities such as the sunshine duration, the latitude and the longitude, the declination angle, the altitude, the cloudiness, the ambient temperature, the relative humidity, the atmospheric pressure, and precipitate water vapor. 2,19,20,[26][27][28][29]31,35,36 The models for the monthly average daily global horizontal solar radiation can be classified in the following categories, see Table I: 31 The models based on sunshine duration are the most widely studied and used in research studies. These models can be classified in four main groups: 19,20 • Linear models-based on a modified form of the Angstr€ om equation.…”