Estimation of Global Solar Radiation Using Artificial Neural Networks in Abu Dhabi City, United Arab Emirates

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Maitha Al-Shamisi ◽  
Ali Assi ◽  
Hassan Hejase
Keyword(s):  
Solar Radiation ◽  
United Arab Emirates ◽  
Meteorological Data ◽  
Geographical Location ◽  
Global Solar Radiation ◽  
Abu Dhabi ◽  
Accurate Estimation ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The geographical location (Latitude: 24 deg 28′ N and Longitude: 54 deg 22′ E) of Abu Dhabi city in the United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for the estimation of monthly mean global solar radiation (GSR) on a horizontal surface in Abu Dhabi. The ANN models are presented and implemented on a 16-yr measured meteorological data set for Abu Dhabi comprising the maximum daily temperature, mean daily wind speed, mean daily sunshine hours, and mean daily relative humidity between 1993 and 2008. The meteorological data between 1993 and 2003 are used for training the ANN and data between 2004 and 2008 are used for testing the estimated values. Multilayer perceptron (MLP) and radial basis function (RBF) are used as ANN learning algorithms. The results attest to the capability of ANN techniques and their ability to produce accurate estimation models.

scholarly journals Using the artificial neural networks for prediction and validating solar radiation

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Zahraa E. Mohamed
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Horizontal Surface ◽  
The Other ◽  
Rate Coefficients ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Statistical Indicators ◽  
Better Than

AbstractThe main objective of this paper is to employ the artificial neural network (ANN) models for validating and predicting global solar radiation (GSR) on a horizontal surface of three Egyptian cities. The feedforward backpropagation ANNs are utilized based on two algorithms which are the basic backpropagation (Bp) and the Bp with momentum and learning rate coefficients respectively. The statistical indicators are used to investigate the performance of ANN models. According to these indicators, the results of the second algorithm are better than the other. Also, model (6) in this method has the lowest RMSE values for all cities in this study. The study indicated that the second method is the most suitable for predicting GSR on a horizontal surface of all cities in this work. Moreover, ANN-based model is an efficient method which has higher precision.

scholarly journals Estimation of Global Solar Radiation in Mubi, Nigeria

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Ogbaka D.T ◽  
Bassi H ◽  
Lami D.S ◽  
Tahir M.A
Keyword(s):  
Solar Radiation ◽  
Global Radiation ◽  
Energy System ◽  
Global Solar Radiation ◽  
Solar Irradiation ◽  
Ann Model ◽  
Acceptable Error ◽  
Solar Radiation Intensity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Application of solar energy system requires having knowledge about solar irradiation potential in different locations. This study therefore used artificial neural networks for predicting solar global radiation by using metrological data. There is no report about prediction of solar radiation potential for Mubi by using Artificial Neural Network (ANN) method. It is very encouraging to observe a very fine agreement between the measured and estimated values shown in study. The ANN Model is considered the best relation for estimating the global solar radiation intensity for Mubi region with an acceptable error. The MSE, RMSE, MBE, MABE and MAPE values are 0.930, 0.964, 0.3358 MJm−2day−1, 0.8175 MJm−2day−1 and 19.30%, respectively. The ANN models appear auspicious for estimating the Global Solar Radiation in the locations where there are no solar radiation measurement stations.

scholarly journals Global Solar Radiation Forecasting using Artificial Neural Network and Support Vector Machine

2021 ◽  
Vol 2129 (1) ◽  
pp. 012079
Author(s):  
Emmanuel Philibus ◽  
Roselina Sallehuddin ◽  
Yusliza Yussof ◽  
Lizawati Mi Yusuf
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Solar Radiation ◽  
Land Surface ◽  
Statistical Tests ◽  
Global Solar Radiation ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Global solar radiation (GSoR) forecasting involves predicting future energy from the sun based on past and present data. Literature reveals that not all meteorological stations record solar radiation, some equipments are faulty, and are not available in every location due to high cost. Hence, the need to predict and forecast using predictors such as land surface temperature (LST). Satellite data when were used to complement ground-based stations have been yielding good results. Different artificial intelligence (AI) methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) present different forecasting performances. Motivated by existing literature-related contradictions on the performance superiority of ANN and SVM in GSoR forecasting, the two techniques were compared based on several statistical tests. Experimental results show that ANN outperformed SVM by 2.9864% accuracy, making it superior in the forecast of GSoR.

scholarly journals Performance Evaluation of Artificial Neural Networks in Estimating Global Solar Radiation, Case Study: New Borg El-arab City, Egypt

2017 ◽  
Vol 2 (1) ◽  
pp. 16
Author(s):  
Gasser E. Hassan ◽  
Mohamed A. Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Global Solar Radiation ◽  
Data Set ◽  
Primary Input ◽  
Artificial Neural ◽  
Availability Assessment

The most sustainable source of energy with unlimited reserves is the solar energy, which is the main source of all types of energy on earth. Accurate knowledge of solar radiation is considered to be the first step in solar energy availability assessment. It is also the primary input for various solar energy applications. The unavailability of the solar radiation measurements for several sites around the world leads to proposing different models for predicting the global solar radiation. Artificial neural network technique is considered to be an effective tool for modelling nonlinear systems and requires fewer input parameters. This work aims to investigate the performance of artificial neural network-based models in estimating global solar radiation. To achieve this goal, measured data set of global solar radiation for the case study location (Lat. 30˚ 51 ̀ N and long. 29˚ 34 ̀ E) are utilized for model establishment and validation. Mostly, common statistical indicators are employed for evaluating the performance of these models and recognizing the best model. The obtained results show that the artificial neural network models demonstrate promising performance in the prediction of global solar radiation. In addition, the proposed models provide superior consistency between the measured and estimated values.

Global Radiation for Solar Architecture Design of Taiwan

2010 ◽  
Vol 44-47 ◽  
pp. 1853-1861 ◽  
Author(s):  
W.S. Ou ◽  
K.T. Huang ◽  
Chi Chang Liao
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Global Radiation ◽  
Geographical Location ◽  
Global Solar Radiation ◽  
Distribution Diagram ◽  
Climate Conditions ◽  
Radiation Data ◽  
Site Planning ◽  
Central Weather Bureau

Solar radiation data is an important consideration factor in building environment planning. This study focused on the regional characteristics of global solar radiation of Taiwan. The research utilized the raw meteorological data meseaursed by the Central Weather Bureau to establish reliable solar radiation data of weather stations by means of statistic analysis. A total of weather station’s data are used where their geographical location are evenly distributed from northern to southern Taiwan. The results of this study will presented the diagrams of annual and monthly averaged solar radiation in Taiwan. Geographical distribution of inter-annual trend of global solar radiation during this period was also presented in this paper. It can be used for further study of the climate zoning comparing with other climate conditions. Furthermore, researchers of solar cell design, building energy, shading design, site planning, etc. can utilize the distribution diagram to fetch reliable solar radiation values to carry on reasonable quantitative analysis.

scholarly journals Prediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks

2017 ◽  
Vol 14 (2) ◽  
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Narx Models ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive ◽  
Yearly Data

In this study, three Artificial Neural Network (ANN) models (Feedforward network, Elman, and Nonlinear Autoregressive Exogenous (NARX)) were used to predict hourly solar radiation in Amman, Jordan. The three models were constructed and tested by using MATLAB software. Meteorological data for the years from 2000 to 2010 were used to train the ANN while the yearly data of 2011 was used to test it. It was found that ANN technique may be used to estimate the hourly solar radiation with an excellent accuracy, and the coefficient of determination of Elman, feedforward and NARX models were found to be 0.97353, 0.97376, and 0.99017, respectively. The obtained results showed that NARX model has the best ability to predict the required solar data, while Elman and feedforward models have the lowest ability to predict it.

scholarly journals Prediction of Global Solar Radiation from Sunrise Duration Using Regression Functions

2021 ◽  
Author(s):  
A. Haj Ismail ◽  
Keyword(s):  
Solar Radiation ◽  
United Arab Emirates ◽  
Theoretical Models ◽  
Global Solar Radiation ◽  
Abu Dhabi ◽  
Coefficient Of Determination ◽  
Sunshine Hours ◽  
The Mean ◽  
Statistical Indicators ◽  
Good Agreement

The paper estimates the global solar radiation in Dubai and Abu Dhabi meteorological stations in the United Arab Emirates. Theoretical models based on Angstrom-Prescott have been applied on data of sunshine hours for the period from 2010 to 2019. The models are developed using 2010 - 2018 data and validated by comparing to the 2019 data. Theoretical solar radiation estimates the maximum of global solar radiation to be in June for both stations, which is in good agreement with the actual data. The performance of the model is tested using statistical indicators such as the coefficient of determination R2 and the Mean Root Square RMSE. The results show that the models were effective enough to describe the global solar radiation with overall R2 of 0.8874 and 0.8706, RMSE of 0.0258 and 0.0241, MBE of 0.0412 and 0.0376, and MPE of 0.2371 and 0.2318, for Abu Dhabi and Dubai meteorological stations, respectively.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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