A novel elephant herd optimization model with a deep extreme Learning machine for solar radiation prediction using weather forecasts

2022 ◽  
Author(s):  
K. Nageswara Reddy ◽  
M. Thillaikarasi ◽  
B. Siva Kumar ◽  
T. Suresh
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Optimization Model ◽  
Weather Forecasts ◽  
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.

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

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

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 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
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