Open AccessEstimating the Annual Global Solar Radiation In Three Jordanian Cities by Using Air Temperature Data
Author(s) -
I. M. Abolgasem
Publication year - 2016
Publication title -
al-ṭāqaẗ al-s̆amsiyyaẗ wa-al-tanmiyyaẗ al-mustadāmaẗ/solar energy and sustainable development
Language(s) - English
Resource type - Journals
eISSN - 2414-6013
pISSN - 2411-9636
DOI - 10.51646/jsesd.v5i2.61
Subject(s) - artificial neural network , radiation , meteorology , solar energy , environmental science , data set , training set , set (abstract data type) , air temperature , computer science , machine learning , engineering , geography , artificial intelligence , physics , quantum mechanics , electrical engineering , programming language
Estimating solar radiation is an imperative requirement for solar energy development in Jordan. In this paper, a quantitative approach, based on Artificial Neural Network, was developed for estimating the annual global solar radiation of three Jordanian cities: Amman, Irbid and Aqaba. These cities are currently witnessing huge development and increasing demand for energy supply. Using a set of known meteorological parameters, two Artificial Neural Network (ANN) models with different architectures, called case 1 and case 2, fed with three types of learning algorithms for data training and testing, were designed to identifythe optimum conditions for obtaining reliable and accurate prediction of the solar radiation. The results showed that model case 1 performed generally better in terms of predicting the annual GSR (96%) compared to model case 2 (95%). Furthermore, the algorithms LM and SCG, in general, ensured the highest efficiency in training and testing the data in the designed models compared to the GDX algorithm. Therefore, model case 1, designed with one of these two algorithms, is selected as the optimal model design that is able to compute with high accuracy the annual solar radiation for the three studied cities.