Intelligent approaches for global horizontal irradiance forecasting in Saudi Arabia: Benchmarking deep learning coupled with RMSprop optimization for solar power systems

Hassan Z. Al Garni

doi:10.1016/j.rineng.2026.110877

Intelligent approaches for global horizontal irradiance forecasting in Saudi Arabia: Benchmarking deep learning coupled with RMSprop optimization for solar power systems

2026-05-04

0

OA

AI

H

Hassan Z. Al Garni

A

Arunachalam Sundaram

M

Mohamed E. Zayed *

A

Abdullahi Abubakar Mas’ud

S

Shafiqur Rehman *

M

Mohammed Abdul Baseer

M

Mubarak A. Alanazi

S

Saud Al-Shammari

DOI：10.1016/j.rineng.2026.110877

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Abstract

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• Estimation of global horizontal irradiance (GHI) by a newly hybrid AI model. • Hybrid multilayer perceptron neural network (MPANN) and RMSprop optimizer is established. • Models evaluated stepwise inputs reduction in GHI prediction. • RMSprop algorithm is applied for fine-tunning MPANN model. • MPANN- RMSprop had superior accuracy in GHI forecasting.

Keywords:

Climate-sensitive regions

Machine learning-aided modeling

Global horizontal irradiance forecasting

RMSprop optimization

Input features selection

Decision making criteria

Intelligent approaches for global horizontal irradiance forecasting in Saudi Arabia: Benchmarking deep learning coupled with RMSprop optimization for solar power systems

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