Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting
| Autor: | Stefano Alessandrini, Luca Delle Monache, Federica Davo, Simone Sperati, Maria Teresa Vespucci, Davide Airoldi |
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| Rok vydání: | 2016 |
| Předmět: |
Meteorology
020209 energy Principal component analysis 02 engineering and technology Environment Solar irradiance Analog ensemble Forecasting Neural network Wind power Renewable Energy Sustainability Materials Science(all) 0202 electrical engineering electronic engineering information engineering General Materials Science Physics::Atmospheric and Oceanic Physics Artificial neural network business.industry Renewable Energy Sustainability and the Environment Numerical weather prediction Earth system science Regional Atmospheric Modeling System Environmental science Mesonet Settore MAT/09 - Ricerca Operativa business |
| Zdroj: | Solar Energy. 134:327-338 |
| ISSN: | 0038-092X |
| DOI: | 10.1016/j.solener.2016.04.049 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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