Estimating the Annual Global Solar Radiation In Three Jordanian Cities by Using Air Temperature Data

Estimating solar radiation is an imperative requirement for solar energy development in Jordan. In this paper, a quantitative approach, based on Artifiial Neural Network, was developed for estimating the annual global solar radiation of three Jordanian cities: Amman, Irbid and Aqaba. Thse cities are currently witnessing huge development and increasing demand for energy supply. Using a set of known meteorological parameters, two Artifiial Neural Network (ANN) models with diffrent architectures, called case 1 and case 2, fed with three types of learning algorithms for data training and testing, were designed to identify the optimum conditions for obtaining reliable and accurate prediction of the solar radiation. Th 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 effiency in training and testing the data in the designed models compared to the GDX algorithm. Threfore, 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.

Download Full-text

Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks

Journal of Engineering Research ◽

10.36909/jer.v9i2.8455 ◽

2021 ◽

Vol 9 (2) ◽

Author(s):

Mohammed Ali Jallal ◽

◽

Samira Chabaa ◽

Abdelouhab Zeroual ◽

◽

...

Keyword(s):

Neural Network ◽

Solar Radiation ◽

Solar Energy ◽

Meteorological Parameters ◽

Energy Systems ◽

Global Solar Radiation ◽

Acquisition Time ◽

Feedback Connection ◽

Intelligent Models ◽

The City

Precise global solar radiation (GSR) measurements in a given location are very essential for designing and supervising solar energy systems. In the case of rarity or absence of these measurements, it is important to have a theoretical or empirical model to compute the GSR values. Therefore, the main goal of this work is to offer, to designers and engineers of solar energy systems, an appropriate and accurate way to predict the half-hour global solar radiation (HHGSR) time series from some available meteorological parameters (relative humidity, air temperature, wind speed, precipitation, and acquisition time vector in half-hour scale). For that purpose, two intelligent models are developed: the first one is a multivariate dynamic neural network with feedback connection, and the second is a multivariate static neural network. The database used to build these models was recorded in Agdal’s meteorological station in Marrakesh, Morocco, during the years of 2013 and 2014, and it was divided into two subsets. The first subset is used for training and validating the models, and the second subset is used for testing the efficiency and the robustness of the developed models. The obtained results, in terms of the statistical performance indicators, demonstrate the efficiency of the developed forecasting models to accurately predict the HHGSR parameter in the city of Marrakesh, Morocco.

Download Full-text

Sensitivity analysis based artificial neural network approach for global solar radiation prediction in India

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v20.i1.pp31-38 ◽

2020 ◽

Vol 20 (1) ◽

pp. 31

Author(s):

Rajasekaran Meenal ◽

A. Immanuel Selvakumar ◽

Prawin Angel Michael ◽

Ekambaram Rajasekaran

Keyword(s):

Neural Network ◽

Sensitivity Analysis ◽

Artificial Neural Network ◽

Solar Radiation ◽

Sunshine Duration ◽

Global Solar Radiation ◽

Day Length ◽

Input Parameters ◽

Ann Models ◽

Artificial Neural

The objective of this paper is to build an artificial neural network model to predict Global Solar Radiation (GSR) with improved accuracy using less number of best input parameters selected using sensitivity analysis. In this work, the input parameters used for training the artificial neural network (ANN) models are bright sunshine duration, maximum and minimum temperature, day length, months, extra terrestrial radiation (H0), relative humidity and geographical parameters of the locations namely the latitude and longitude. Sensitivity analysis is used to discover how the output data are influenced by the changeability of the input data.Three ANN models namely T-ANN, S-ANN and TS-ANN are proposed with most suitable input parameters selected using sensitivity analysis. The principle of this feature selection using sensitivity analysis is to improve the prediction accuracy of solar radiation models with less number of inputs. The proposed ANN model is also tested under noisy data and proved that ANN is able to perform reasonably good in GSR prediction on practical applications where the data is affected by noise caused by errors on measuring, fault of data acquisition system, recording problems, and so on.

Download Full-text

Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation

Energies ◽

10.3390/en13102488 ◽

2020 ◽

Vol 13 (10) ◽

pp. 2488

Author(s):

Olubayo M. Babatunde ◽

Josiah L. Munda ◽

Yskandar Hamam

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Radiation ◽

Meteorological Parameters ◽

Production Systems ◽

Global Solar Radiation ◽

Maximum Temperature ◽

Remote Communities ◽

Artificial Neural ◽

Solar Powered

The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and R 2 of 0.8254.

Download Full-text