Prediction of Solar Radiation Intensity using Extreme Learning Machine

Abstract

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.

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

“…The successful utilization of extreme learning machines (ELMs) in the prediction of SR has been achieved by employing diverse climatic input parameters [16][17][18][19][20]. Suyono et al (2018) used an ELM for predicting solar radiation in Basel, Switzerland, the results of which are available to the public, and compared the results with multilinear regression (MLR). The ELM model improved the accuracy of MLR by 15.29% and 24.79% with respect to root mean square errors and mean absolute errors [18].…”

“…Suyono et al (2018) used an ELM for predicting solar radiation in Basel, Switzerland, the results of which are available to the public, and compared the results with multilinear regression (MLR). The ELM model improved the accuracy of MLR by 15.29% and 24.79% with respect to root mean square errors and mean absolute errors [18]. Hou et al (2018) used an ELM model integrated with a variable forgetting factor (FOS-ELM) for predicting global SR at Bur Dedougou, Burkina Faso.…”

Microgrid Design in Electricity Supply in Paper Factories

“…It is located at 6008' N to 11015' LS and 94045' BT to 141005' BT, which is on the equator with a relatively high solar radiation intensity averaging around 4.8 kWh / m² per day throughout Indonesia. Based on solar intensity data in Indonesia is a potential that should be utilized to produce electrical energy optimally (Syahputra & Soesanti, 2020) (Syahputra & Soesanti, 2021) (Suyono et al, 2018) (Zulkarnain & Zambak, 2021) (Fitriaty & Shen, 2018) (Susan & Wardhani, 2020) (Revy et al, 2022) (Edward & Dewi, 2019).…”

Sensitivity analysis based artificial neural network approach for global solar radiation prediction in India

“…Various ANN models [10][11][12][13][14][15] have been developed for the prediction of daily and monthly global solar radiation. ANN [16][17][18][19] is the most commonly used technique for the prediction of GSR and recently machine learning techniques like support vector machine [20] is used for solar radiation prediction. Identification of the best suitable input variables is the main research area in solar radiation prediction.…”