Journal
SOLAR ENERGY
Volume 134, Issue -, Pages 327-338Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2016.04.049
Keywords
Analog ensemble; Neural network; Principal component analysis; Solar irradiance; Wind power; Forecasting
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Funding
- Research Fund for the Italian Electrical System
- U.S. Department of Energy's SunShot Initiative [DE-EE0006016]
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This work explores a Principal Component Analysis (PCA) in combination with two post-processing techniques for the prediction of wind power produced over Sicily, and of solar irradiance measured by Oklahoma Mesonet measurements' network. For wind power, the study is conducted over a 2-year long period, with hourly data of the aggregated wind power output of the Sicily island. The 0-72 h wind predictions are generated with the limited-area Regional Atmospheric Modeling System (RAMS), with boundary conditions provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic forecast. For solar irradiance, we consider daily data of the aggregated solar radiation energy output (based on the Kaggle competition dataset) over an 8-year long period. Numerical Weather Prediction data for the contest come from the National Oceanic & Atmospheric Administration- Earth System Research Laboratory (NOAA/ESRL) Global Ensemble Forecast System (GEFS) Reforecast Version 2. The PCA is applied to reduce the datasets dimension. A Neural Network (NN) and an Analog Ensemble (AnEn) post-processing are then applied on the PCA output to obtain the final forecasts. The study shows that combining PCA with these post-processing techniques leads to better results when compared to the implementation without the PCA reduction. (C) 2016 Elsevier Ltd. All rights reserved.
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