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MPF-Net: A computational multi-regional solar power forecasting framework

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  • Mehmood, Faiza
  • Ghani, Muhammad Usman
  • Asim, Muhammad Nabeel
  • Shahzadi, Rehab
  • Mehmood, Aamir
  • Mahmood, Waqar

Abstract

Short-term solar irradiance forecasting plays a pivotal role in the effective integration of significantly fluctuating solar power into power grids. Existing computational approaches lack to investigate which climate parameter/s influence the most in attaining the optimal forecasting performance. The paper in hand utilizes diverse feature selection approaches to find the optimal subset of features. Using selected subset of features, a rigorous experimentation is performed with 12 adopted machine learning and 10 newly developed deep learning based regressors for most reliable global horizontal irradiance measurements of 9 different regions of Pakistan using 4 evaluation measures. Further, to attain better predictive performance of solar irradiance, we reap the benefits of different individual regressors and present a robust multi regional meta-regressor. Among machine and deep learning based regressors, proposed meta-regressor along with optimal subset of feature/s achieves the best R2 score of 98% for 6 regions and 97% for other 3 regions of Pakistan. MPF-Net as web service is accessible here.

Suggested Citation

  • Mehmood, Faiza & Ghani, Muhammad Usman & Asim, Muhammad Nabeel & Shahzadi, Rehab & Mehmood, Aamir & Mahmood, Waqar, 2021. "MPF-Net: A computational multi-regional solar power forecasting framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:rensus:v:151:y:2021:i:c:s1364032121008376
    DOI: 10.1016/j.rser.2021.111559
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    Cited by:

    1. Hoyos-Gómez, Laura S. & Ruiz-Muñoz, Jose F. & Ruiz-Mendoza, Belizza J., 2022. "Short-term forecasting of global solar irradiance in tropical environments with incomplete data," Applied Energy, Elsevier, vol. 307(C).
    2. Yin, Linfei & Cao, Xinghui & Liu, Dongduan, 2023. "Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 332(C).
    3. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).

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