Mitigating errors in satellite solar irradiation using a sequential empirical-ANN model for four cities across Central and Northern Pakistan.

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. First 6 empirical models were developed for each city, and then the output of the best model for each city was incorporated into the ANN model. Ground-measured data from the Energy Sector Management Assistance Program (ESMAP) and satellite data from NASA's Prediction of Worldwide Energy Resources (POWER) database were used. Using meteorological parameters such as temperature, humidity, precipitation, clearness index, and day number, empirical models were formulated, and their outputs fed into ANN models. The sequential Empirical-ANN (SE-ANN) model achieved mean absolute percentage errors (MAPE) ranging from 5.86% in Bahawalpur to 17.09% in Lahore, outperforming satellite data, which achieved a maximum MAPE of 25.30%. Root mean square errors (RMSE) and Mean absolute error (MAE) decreased significantly, with values as low as 0.43 kWh/m