Comparative Assessment of Temperature Based ANN and Angstrom Type Models for Predicting Global Solar Radiation

Abstract

Abstract. In this study, temperature based artificial neural network (ANN) models and Angstrom type models for predicting global solar radiation were developed for selected locations in Nigeria.The ANN models were standard multi-layered feed forward, back-propagation neural networks trained with the Levenberg Marquardt algorithm using seventeen years data collected from Nigerian Meteorological Agency (NIMET ), Abuja, Nigeria and tested with twenty-two years monthly averaged data downloaded from National Aeronautical Space Administration (NASA) online database. The network inputs were latitude, longitude, elevation, month, maximum ambient temperature (Tmax) and minimum ambient temperature (Tmin), while monthly average global solar radiation was the network output.The Angstrom type empirical models correlated global solar radiation with minimum and maximum ambient temperatures. The performance of the models were evaluated using statistical performance indicators, namely RM SE, MBE, R 2 and rank score. The coefficients of determination (R 2 ) of the ANN models were always greater than 99% for all the selected locations while the highest coefficient of determination for the empirical models was 89%. The temperature-based ANN models were thus shown to deliver superior and more reliable outcomes in comparison with the empirical models.

Performance evaluation of solar radiation equations for estimating reference evapotranspiration (ETo) in a humid tropical environment

“…Daily weather data (solar radiation, minimum temperature, maximum temperature, relative humidity and wind speed) as summarized in Table 1, was obtained from the archives of NASA (National Aeronautics Space Administration) Prediction of Worldwide Energy Resource, POWER (https://power.larc.nasa.gov/) for a 32-year period (July 1983-December 2015). Similar studies [ADARAMO-LA 2012;CHINEKE 2008;EGEONU et al 2015;OKUNDA-MIYA et al 2016] have adopted this method. The data was further checked for error, quality assessment, inconsistencies and missing data as recommended by World Meteorological Organization [WMO 1987] andALLEN [1996].…”

Assessment of eight reference evapotranspiration (ETo) methods considering Köppen climate class in Iran

“…The MBE and RMSE from each ET o method at each station were normalized by dividing both MBE and RMSE by the average of the PM evapotranspiration value at each station. A rank score was obtained for each method at each station by (Mubiru et al 2007, Egeonu et al 2015:…”