Sharul Sham Dol

Abstract Accurate prediction of global horizontal irradiance (GHI) is essential for optimizing solar energy utilization. Satellite-based datasets often overestimate GHI, leading to errors that hinder reliable solar resource assessment. This study presents a sequential modeling approach combining newly developed empirical models with artificial neural networks (ANN) to reduce errors in satellite-derived GHI for four cities in Pakistan: Bahawalpur, Islamabad, Lahore, and Peshawar.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.

Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 3 September 2024 / Approved: 4 September 2024 / Online: 5 September 2024 (04:51:11 CEST) Moustafa, M. H.; Khashwain, H. S.; Dol, S. S.; Al Ramahi, A. Enhancing Aerodynamic Performance in UAV Design: CFD Analysis. Preprints 2024, 2024090340. https://doi.org/10.20944/preprints202409.0340.v1 Moustafa, M. H.; Khashwain, H. S.; Dol, S. S.; Al Ramahi, A. Enhancing Aerodynamic Performance in UAV Design: CFD Analysis.