Abstract
The availability of short-term forecast weather model for a particular country or region is essential for operation planning of energy systems. This paper presents the first step by a group of researchers at UAE University to establish a weather model for the UAE using the weather data for at least 10 years and employing various models such as classical empirical models, artificial neural network (ANN) models, and time-series regression models with autoregressive integrated moving-average (ARIMA). This work uses time-series regression with ARIMA modeling to establish a model for the mean daily and monthly global solar radiation (GSR) for the city of Al-Ain, United Arab Emirates. Time-series analysis of solar radiation has shown to yield accurate average long-term prediction performance of solar radiation in Al-Ain. The model was built using data for 10 years (1995–2004) and was validated using data of three years (2005–2007), yielding deterministic coefficients (R2) of 92.6% and 99.98% for mean daily and monthly GSR data, respectively. The low corresponding values of mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) confirm the adequacy of the obtained model for long-term prediction of GSR data in Al-Ain, UAE.
Highlights
Gulf Corporation Countries (GCC) that include the United Arab Emirates (UAE) are seeking to make better use of their abundant sustainable energy sources and solar energy
The availability of a solar radiation model in a particular region is very useful in estimating the amount of power that can be generated from a particular solar energy system
The solar radiation data taken in Al-Ain, UAE, are analyzed using time-series regression with Box-Jenkins ARMA model
Summary
Gulf Corporation Countries (GCC) that include the United Arab Emirates (UAE) are seeking to make better use of their abundant sustainable energy sources and solar energy. Techniques used for modeling the mean daily global solar radiation include empirical regression, neural network, and time-series ARIMA models. Input variables to the network included latitude, longitude, altitude, and sunshine duration Their RBF model is compared with MLP ANN technique and other classical empirical models such as Angstrom-type equations. This work uses examples with classical empirical regression techniques and time-series techniques to predict the monthly average daily GSR data in Al-Ain, UAE. In this timeseries regression technique, the deterministic component was modeled by decomposing it into a multiple linear regression component as a function of the available weather variables plus a cyclical component accounting for the annual periodicity and a linear trend. The final objective is to come up with a good national weather model capable of predicting the mean monthly GSR for the whole UAE within an acceptable prediction error
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
