scholarly journals An Artificial Neural Network Model for Estimating Daily Solar Radiation in Northwest Nigeria

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Salisu Aliyu ◽  
Aminu S Zakari ◽  
Muhammad Ismail ◽  
Mohammed A Ahmed
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Network Data ◽  
Radiation Data ◽  
Testing Dataset ◽  
The World ◽  
Artificial Neural

Solar energy has attracted enormous attention as it plays an essential role in meeting the ever growing sustainable and environmental friendly energy demand of the world. Due to the high cost of calibration and maintenance of designated measuring instruments, solar radiation data are limited not only in Nigeria but in most parts of the world. The optimal design of solar energy systems requires accurate estimation of solar radiation. Existing studies are focused on the analysis of monthly or annual solar radiation with less attention paid to the determination of daily solar radiation. Estimating daily solar radiation envisages high quality and performance of solar systems. In this paper, an Artificial Neural Network data mining model is proposed for estimating the daily solar radiation in Kano, Kaduna and Katsina, North West Nigeria. Daily Solar radiation data for 21years collected from the Nigerian Metrological Agency were used as training/testing data while developing the model. Two statistical indicators: coefficient of determination (R2) and the root mean square error (RMSE) were used to evaluate the model. An RMSE of 0.47 and 0.48 was obtained for the training and testing dataset respectively, while an R2 of 0.78 was obtained for both the training and testing dataset. The overall results showed that artificial neural network exhibits excellent performance for the estimation of daily solar radiation.Keywords— Artificial Neural Network, Data mining, Solar Radiation 

Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

2016 ◽  
Vol 128 (1-2) ◽  
pp. 439-451 ◽  
Author(s):  
Maamar Laidi ◽  
Salah Hanini ◽  
Ahmed Rezrazi ◽  
Mohamed Redha Yaiche ◽  
Abdallah Abdallah El Hadj ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Radiation Data ◽  
Artificial Neural

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.

scholarly journals A Novel Model Based on Square Root Elastic Net and Artificial Neural Network for Forecasting Global Solar Radiation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
He Jiang ◽  
Yao Dong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Data ◽  
Complex Model ◽  
Elastic Net ◽  
Square Root ◽  
Elman Neural Network ◽  
Artificial Neural

In recent years, solar energy has attracted a great deal of attentions from scientific researchers because it is a clean and renewable form of energy. To make good use of solar energy, an effective way to forecast solar radiation is essential to guarantee the reliability of grid-connected photovoltaic installations. Although an artificial neural network (ANN) is of great importance, irrelevant variables are utilized which results in complex model and intractable computation cost. To remove these irrelevant variables, the combination of variable selection methods and ANN are applied. However, how to select the regularization parameters in these techniques is challenging. This paper successfully investigates a square root elastic net- (SREN-) based approach to tackle this challenge and selects all the important variables. An Elman neural network (ENN) is constructed with the important variables selected by SREN as inputs. Based on meteorological data, SRENENN has been developed for 1-year period in Xinjiang area of China. The present model delivers superior relationship between the estimated and measure values.

scholarly journals A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Tamer Khatib ◽  
Wilfried Elmenreich
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Percentage Error ◽  
Data Generation ◽  
Error Statistics ◽  
Radiation Data ◽  
Proposed Model ◽  
Artificial Neural ◽  
Generalized Regression

This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that the proposed model has better prediction accuracy compared to some empirical and statistical models. Two error statistics are used in this research to evaluate the proposed model, namely, mean absolute percentage error and root mean square error. These values for the proposed model are 11.8% and −3.1%, respectively. Finally, the proposed model shows better ability in overcoming the sophistic nature of the solar radiation data.

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 ◽  
Neural Network Models ◽  
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, and it is 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 dataset 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.

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