A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

• DOI: 10.18280/ria.340505
• Publication Date: 2020-12-02T06:18:30Z
• Authors: Mohammed Ali Jallal; Abdessalam El Yas...; Samira Chabaa; Abdelouhab Zeroual; Saida Ibnyaich
• Keywords: Pyranometer

Nowadays, the studies that address solar radiation (SR) forecasting tend to focus on implementation of conventional techniques. This provides good results, but researchers should creation new methodologies help us in going further and boost prediction accuracy SR data. The prime aim this research study is propose an efficient deep learning (DL) algorithm can handle nonlinearities dynamic behaviors meteorological data, generate accurate real-time hourly global (GSR) data city El Kelaa des Sraghna (32°2’53”N 7°24’30”W), Morocco. proposed DL integrates model named Elman neural network with a input configuration-based autoregressive process order learn from seasonal patterns historical measurements, actual measurements air temperature. attained performance proves reliability forecast GSR time series case missing values detection or pyranometer damage. Hence, electrical power engineers adopt tool improve integration resources into grid system.