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Title: Ensemble Methods for Probabilistic Solar Power Forecasting: A Comparative Study
To guide the selection of probabilistic solar power forecasting methods for day-ahead power grid operations, the performance of four methods, i.e., Bayesian model averaging (BMA), Analog ensemble (AnEn), ensemble learning method (ELM), and persistence ensemble (PerEn) is compared in this paper. A real-world hourly solar generation dataset from a rooftop solar plant is used to train and validate the methods under clear, partially cloudy, and overcast weather conditions. Comparisons have been made on a one-year testing set using popular performance metrics for probabilistic forecasts. It is found that the ELM method outperforms other methods by offering better reliability, higher resolution, and narrower prediction interval width under all weather conditions with a slight compromise in accuracy. The BMA method performs well under overcast and partially cloudy weather conditions, although it is outperformed by the ELM method under clear conditions.  more » « less
Award ID(s):
1845523
NSF-PAR ID:
10489610
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
ISBN:
978-1-6654-6441-3
Page Range / eLocation ID:
1 to 5
Subject(s) / Keyword(s):
Analog ensemble Bayesian model averaging Ensemble learning probabilistic solar power forecasting
Format(s):
Medium: X
Location:
Orlando, FL, USA
Sponsoring Org:
National Science Foundation
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