scholarly journals Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 12026-12042 ◽  
Author(s):  
Tao Hai ◽  
Ahmad Sharafati ◽  
Achite Mohammed ◽  
Sinan Q. Salih ◽  
Ravinesh C. Deo ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Predictive Model ◽  
Extreme Learning Machine ◽  
Climatic Variability ◽  
Global Solar Radiation ◽  
Variability Analysis ◽  
Learning Machine

scholarly journals Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3415 ◽  
Author(s):  
Muzhou Hou ◽  
Tianle Zhang ◽  
Futian Weng ◽  
Mumtaz Ali ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Renewable Energy Sources ◽  
Information Criterion ◽  
Absolute Error ◽  
Global Solar Radiation ◽  
Machine Model ◽  
Learning Machine

Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error (MAE) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters.

Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach

Solar Energy ◽  
2014 ◽  
Vol 105 ◽  
pp. 91-98 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
C. Casanova-Mateo ◽  
A. Pastor-Sánchez ◽  
M. Sánchez-Girón
Keyword(s):  
Solar Radiation ◽  
Coral Reefs ◽  
Extreme Learning Machine ◽  
Global Solar Radiation ◽  
Learning Machine

A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation

2015 ◽  
Vol 52 ◽  
pp. 1031-1042 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Kasra Mohammadi ◽  
Por Lip Yee ◽  
Dalibor Petković ◽  
Ali Mostafaeipour
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Comparative Evaluation ◽  
Global Solar Radiation ◽  
Learning Machine

Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran

2015 ◽  
Vol 134 ◽  
pp. 109-117 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Kasra Mohammadi ◽  
Hui-Ling Chen ◽  
Ganthan Narayana Samy ◽  
Dalibor Petković ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Global Solar Radiation ◽  
Air Temperatures ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Application of extreme learning machine for estimating solar radiation from satellite data

2013 ◽  
Vol 38 (2) ◽  
pp. 205-212 ◽  
Author(s):  
Mehmet Şahin ◽  
Yılmaz Kaya ◽  
Murat Uyar ◽  
Selçuk Yıldırım
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Satellite Data ◽  
Learning Machine

A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran

2017 ◽  
Vol 38 (23) ◽  
pp. 6894-6909 ◽  
Author(s):  
Seyed Hossein Hosseini Nazhad ◽  
Mohammad Mehdi Lotfinejad ◽  
Malihe Danesh ◽  
Rooh ul Amin ◽  
Shahaboddin Shamshirband
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Empirical Models ◽  
Diffuse Solar Radiation ◽  
Southern Iran ◽  
Learning Machine

scholarly journals Prediction of Solar Radiation Intensity using Extreme Learning Machine

2018 ◽  
Vol 12 (2) ◽  
pp. 691 ◽  
Author(s):  
Hadi Suyono ◽  
Hari Santoso ◽  
Rini Nur Hasanah ◽  
Unggul Wibawa ◽  
Ismail Musirin
Keyword(s):  
Artificial Intelligence ◽  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Training Data ◽  
Good Prediction ◽  
Statistical Probability ◽  
Solar Radiation Intensity ◽  
Good Prediction Accuracy ◽  
Weather And Climate ◽  
Learning Machine

The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m<sup>2</sup> and 84.04 W/m<sup>2</sup>, while using the MLR method they are 141.18 W/m<sup>2</sup> and 104.87 W/m<sup>2</sup> respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.

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.   

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