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

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

Renewable and Sustainable Energy Reviews, 2021, vol. 151, issue C

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.

Keywords: Solar forecasting; Computational methodologies; Machine learning; Feature selection; Expert knowledge induced features; Multi regional (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.rser.2021.111559

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