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<p>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<sup>2</sup>/day and 0.29 kWh/m<sup>2</sup>/day in Bahawalpur compared to 1.07 kWh/m<sup>2</sup>/day for satellite data. The sequential model has reduced the MAPE in satellite data by 36.95% in Peshawar, 36.7% in Lahore, and 31.6% in Bahawalpur. For Islamabad, the Empirical model M6 performed best, reducing the MAPE by 17.6%. The R<sup>2</sup> values for the model range from 0.78 in Lahore to 0.93 in Bahawalpur. The results indicate that the SE-ANN model provides a more accurate and cost-effective alternative to pure satellite or empirical models, improving GHI prediction reliability in data-scarce regions and supporting better solar energy system planning and management.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Shaqeeb Ahmad,
  • Adnan Ibrahim,
  • Sharul Sham Dol,
  • Faisal Nawab,
  • Shoaib Ahmad,
  • Norasikin Ahmad Ludin

摘要

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/m2/day and 0.29 kWh/m2/day in Bahawalpur compared to 1.07 kWh/m2/day for satellite data. The sequential model has reduced the MAPE in satellite data by 36.95% in Peshawar, 36.7% in Lahore, and 31.6% in Bahawalpur. For Islamabad, the Empirical model M6 performed best, reducing the MAPE by 17.6%. The R2 values for the model range from 0.78 in Lahore to 0.93 in Bahawalpur. The results indicate that the SE-ANN model provides a more accurate and cost-effective alternative to pure satellite or empirical models, improving GHI prediction reliability in data-scarce regions and supporting better solar energy system planning and management.