scholarly journals Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
K. Sopian ◽  
M. Mahmoud
Keyword(s):  
Neural Networks ◽  
Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Climate Conditions ◽  
The Neural Networks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Radiation Conditions

This paper presents an assessment for the artificial neural network (ANN) based approach for hourly solar radiation prediction. The Four ANNs topologies were used including a generalized (GRNN), a feed-forward backpropagation (FFNN), a cascade-forward backpropagation (CFNN), and an Elman backpropagation (ELMNN). The three statistical values used to evaluate the efficacy of the neural networks were mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Prediction results show that the GRNN exceeds the other proposed methods. The average values of the MAPE, MBE and RMSE using GRNN were 4.9%, 0.29% and 5.75%, respectively. FFNN and CFNN efficacies were acceptable in general, but their predictive value was degraded in poor solar radiation conditions. The average values of the MAPE, MBE and RMSE using the FFNN were 23%, −.09% and 21.9%, respectively, while the average values of the MAPE, MBE and RMSE using CFNN were 22.5%, −19.15% and 21.9%, respectively. ELMNN fared the worst among the proposed methods in predicting hourly solar radiation with average MABE, MBE and RMSE values of 34.5%, −11.1% and 34.35%. The use of the GRNN to predict solar radiation in all climate conditions yielded results that were highly accurate and efficient.

scholarly journals Using Artificial Neural Networks to Predict Direct Solar Irradiation

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
James Mubiru
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Radiation ◽  
Prediction Model ◽  
Solar Irradiation ◽  
Direct Solar Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Artificial Neural ◽  
Direct Solar Irradiation

This paper explores the possibility of developing a prediction model using artificial neural networks (ANNs), which could be used to estimate monthly average daily direct solar radiation for locations in Uganda. Direct solar radiation is a component of the global solar radiation and is quite significant in the performance assessment of various solar energy applications. Results from the paper have shown good agreement between the estimated and measured values of direct solar irradiation. A correlation coefficient of 0.998 was obtained with mean bias error of 0.005 MJ/m2 and root mean square error of 0.197 MJ/m2. The comparison between the ANN and empirical model emphasized the superiority of the proposed ANN prediction model. The application of the proposed ANN model can be extended to other locations with similar climate and terrain.

scholarly journals Estimation of monthly global solar radiation in Buenos Aires: preliminary analysis

2014 ◽  
Vol 5 (1) ◽  
pp. 669-680
Author(s):  
Susan G. Lakkis ◽  
Mario Lavorato ◽  
Pablo O. Canziani
Keyword(s):  
Solar Radiation ◽  
Buenos Aires ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Estimation Accuracy ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Third Order ◽  
Sunshine Hours

Six existing models and one proposed approach for estimating global solar radiation were tested in Buenos Aires using commonly measured meteorological data as temperature and sunshine hours covering the years 2010-2013. Statistical predictors as mean bias error, root mean square, mean percentage error, slope and regression coefficients were used as validation criteria. The variability explained (R2), slope and MPE indicated that the higher precision could be excepted when sunshine hours are used as predictor. The new proposed approach explained almost 99% of the RG variability with deviation of less than ± 0.1 MJm-2day-1 and with the MPE smallest value below 1 %. The well known Ångström-Prescott methods, first and third order, was also found to perform for the measured data with high accuracy (R2=0.97-0.99) but with slightly higher MBE values (0.17-0.18 MJm-2day-1). The results pointed out that the third order Ångström type correlation did not improve the estimation accuracy of solar radiation given the highest range of deviation and mean percentage error obtained.  Where the sunshine hours were not available, the formulae including temperature data might be considered as an alternative although the methods displayed larger deviation and tended to overestimate the solar radiation behavior.

scholarly journals An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

2021 ◽  
Vol 53 (1) ◽  
pp. 37-53
Author(s):  
Milica Vidak-Vasic ◽  
Lato Pezo ◽  
Vivek Gupta ◽  
Sandeep Chaudhary ◽  
Zagorka Radojevic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coal Ash ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Clay Bricks ◽  
Coal Ashes ◽  
Artificial Neural

This study analyzed the last 20 years` data available on power plant coal ashes used in clay brick production. The statistical analysis has been carried out for a total of 302 cases based on the relevant parameters reported in the literature. The chemical composition of the clays and coal ashes, percentage incorporation and maximum particle size of ash, size of fired samples, peak firing temperature, and the corresponding soaking time were selected as inputs for modeling. The product characteristics i.e. open porosity, water absorption, and compressive strength was taken as output parameters. An artificial neural network model has been developed and showed a satisfactory fit to experimental data and predicted the observed output variables with the overall coefficient of determination (r2) of 0.972 during the training period. Besides, the reduced chi-square, mean bias error, root mean square error, and mean percentage error were utilized to check the correctness of the obtained model, which proved the network generalization capability. The sensitivity analysis of the model suggested that the quantity of Na2O coming from brick clays, the percentages of SiO2 and K2O coming from ashes, and MgO coming from clays were the most influential parameters in descending order for the ash-clay composite bricks` quality, mostly owing to the influence of fluxes during firing.

Evaluation of the regression parameters of the Angstrom-Page model for predicting global solar radiation

2013 ◽  
Vol 24 (2) ◽  
pp. 46-49 ◽  
Author(s):  
Solomon Agbo
Keyword(s):  
Solar Radiation ◽  
Statistical Tests ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Statistical Parameters ◽  
New Model ◽  
Page Model ◽  
Regression Parameters

A simple and empirical model for the estimation of average monthly global solar radiation for a Nigerian location is presented. Regression coefficients satisfying the Angstrom-page model have been obtained using clearness index (KT) and the relative sunshine data for the location. The test of validity of the model was done by evaluating the following statistical parameters: the mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE) and the correlation coefficient (CC). The results obtained from the statistical tests show that the new model is reliable for high precision estimation of global solar radiation. A comparison between the new model and other models is presented.

scholarly journals A Comparative Study of Some Regression Models to Estimate the Global Solar Radiation on a Horizontal Surface from Sunshine Duration and Meteorological Parameters for Ghardaïa Site, Algeria

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Kacem Gairaa ◽  
Yahia Bakelli
Keyword(s):  
Solar Radiation ◽  
Horizontal Plane ◽  
Regression Models ◽  
Meteorological Parameters ◽  
Sunshine Duration ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Quadratic Models

A comparison between some regression correlations for predicting the global solar radiation received on a horizontal plane has been processed. Seven models for estimating the global solar radiation from sunshine duration and two meteorological parameters (air temperature and relative humidity) are presented. The root mean square error (RMSE), mean bias error (MBE), correlation coefficient (CC), and percentage error () have been also computed to test the accuracy of the proposed models. Comparisons between the measured and the calculated values have been made. The results obtained show that the linear and quadratic models are the most suitable for estimating the global solar radiation from sunshine duration, and for the models based on meteorological parameters, Abdalla and Ojosu's models give the best performance with a CC of 0.898 and 0.892, respectively.

Evaluation of Global Solar Radiation using Sunshine Hour, Temperature and Relative Humidity at Low Land Region of Nepal

2020 ◽  
Vol 6 (1) ◽  
pp. 16-24
Author(s):  
U. Joshi ◽  
K.N. Poudyal ◽  
I.B. Karki ◽  
N.P. Chapagain
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Energy Potential ◽  
To Come ◽  
Empirical Constants

The accurate knowledge of solar energy potential is essential for agricultural scientists, energy engineers, architects and hydrologists for relevant applications in concerned fields. It is cleanest and freely available renewable energy measured using CMP6 Pyranometer. However, it is quite challenging to acquire accurate solar radiation data in different locations of Nepal because of the high cost of instruments and maintenances. In these circumstances, it is essential to select an appropriate empirical model to predict global solar radiation for the use of future at low land, Nepalgunj (28.102°N, 81.668°E and alt. 165 masl) for the year 2011-2012. In this paper, six different empirical models have been used based on regression technique, provided the meteorological data. The empirical constants (a = 0.61, b = 0.05, c = -0.0012 and d = -0.017) are obtained to predict Global solar radiation. The values of statistical tools such as mean percentage error, mean bias error, root mean square error, and coefficient of determination obtained for Abdalla model are 1.99%, 0.003 MJ/m2/day, 2.04 MJ/m2/day and 0.74 respectively. Using the error analysis, it is concluded that the Abdalla model is better than others. So the empirical constants of this model are utilized to predict the global solar radiation to the similar geographical sites of Nepal for the years to come and it can be used to estimate the missing data of solar radiation for the respective sites.

scholarly journals Choice of ideal sunshine hour based model to predict global solar radiation in India

MAUSAM ◽  
2021 ◽  
Vol 71 (3) ◽  
pp. 451-466
Author(s):  
SAMANTA SUMAN ◽  
BANERJEE SAON ◽  
PATRA PULAK KUMAR ◽  
MAITI SUDHANSU SEKHAR ◽  
CHATTOPADHYAY NABANSU
Keyword(s):  
Energy Source ◽  
Solar Radiation ◽  
Energy Demand ◽  
Global Solar Radiation ◽  
Exponential Model ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Energy Conversion Systems ◽  
Sunshine Hour

Solar radiation is the key energy source for most of the energy conversion systems, whether it is biological or mechanical. It is also the most fundamental energy source for future energy demand. Like most of the developing countries, India also lacks sufficient instrument facilities to measure global solar radiation (GSR) at recommended spatial interval and alternative approaches must be used to generate GSR data. In the present study, six well known empirical models were tested to estimate the GSR over twelve major cities of India using long-term global solar radiation and bright sunshine hour data. The empirical coefficients have been calculated for all the models and each location using regression analysis method. Daily GSR are then calculated using those regression constants along with statistical analysis. Results reveal that all the models shows close estimation with low mean bias error (MBE), root mean square error (RMSE) and mean percentage error (MPE) values. Among all models, linear exponential and linear logarithmic models are highly recommended for prediction of GSR throughout the country, except Shillong, where Bakircilinear exponential model is recommended. Significance tests i.e., t-test also confirms that this two model produce most significant results than others.

scholarly journals Empirical models for predicting global solar radiation using meteorological parameters for Sokoto, Nigeria

2019 ◽  
Vol 7 (2) ◽  
pp. 48
Author(s):  
Davidson O. Akpootu ◽  
Bello I. Tijjani ◽  
Usman M. Gana
Keyword(s):  
Solar Radiation ◽  
Meteorological Parameters ◽  
Meteorological Data ◽  
Climatic Variability ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Radiation Maximum

The performances of sunshine, temperature and multivariate models for the estimation of global solar radiation for Sokoto (Latitude 13.020N, Longitude 05.250E and 350.8 m asl) located in the Sahelian region in Nigeria were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperatures, sunshine hours, rainfall, wind speed, cloud cover and relative humidity meteorological data during the period of thirty one years (1980-2010). The comparison assessment of the models was carried out using statistical indices of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA). For the sunshine based models, a total of ten (10) models were developed, nine (9) existing and one author’s sunshine based model. For the temperature based models, a total of four (4) models were developed, three (3) existing and one author’s temperature based model. The results of the existing and newly developed author’s sunshine and temperature based models were compared and the best empirical model was identified and recommended. The results indicated that the author’s quadratic sunshine based model involving the latitude and the exponent temperature based models are found more suitable for global solar radiation estimation in Sokoto. The evaluated existing Ångström type sunshine based model for the location was compared with those available in literature from other studies and was found more suitable for estimating global solar radiation. Comparing the most suitable sunshine and temperature based models revealed that the temperature based models is more appropriate in the location. The developed multivariate regression models are found suitable as evaluation depends on the available combination of the meteorological parameters based on two to six variable correlations. The recommended models are found suitable for estimating global solar radiation in Sokoto and regions with similar climatic information with higher accuracy and climatic variability.   

scholarly journals New temperature dependent models for estimating global solar radiation across the midland climatic zone of Nigeria

2019 ◽  
Vol 7 (2) ◽  
pp. 70
Author(s):  
Davidson O. Akpootu ◽  
Bello I. Tijjani ◽  
Usman M. Gana
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Climatic Zone ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Temperature Dependent ◽  
Statistical Validation

Authentic information of the availability of global solar radiation is significant to agro/hydro meteorologists, atmospheric Physicists and solar energy engineers for the purpose of local and international marketing, designs and manufacturing of solar equipment. In this study, five new proposed temperature dependent models were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperature meteorological data during the period of thirty one years (1980-2010). The new models were compared with three existing temperature dependent models (Chen et al., Hargreaves and Samani and Garcia) using seven different statistical validation indicators of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) to ascertain the suitability of global solar radiation estimation in five different locations (Zaria, Bauchi, Jos, Minna and Yola) situated in the Midland climatic zone of Nigeria. In each location, the result shows that a new empirical regression model was found more accurate when compared to the existing models and are therefore recommended for estimating global solar radiation in the location and regions with similar climatic information where only temperature data are available. The evaluated existing Hargreaves and Samani and Garcia temperature based models for Jos were compared to those available in literature and was found more suitable for estimating global solar radiation for the location. The comparison between the measured and estimated temperature dependent models depicts slight overestimation and underestimation in some months with good fitting in the studied locations. However, the recommended models give the best fitting.   

scholarly journals Solar Energy Prediction for Malaysia Using Artificial Neural Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
K. Sopian ◽  
M. Mahmoud
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Solar Irradiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Clearness Index ◽  
Energy Prediction ◽  
Global Solar Irradiation ◽  
Artificial Neural

This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

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