Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting

AMS Citation:
Davò, F., S. Alessandrini, S. Sperati, L. Delle Monache, D. Airoldi, and M. T. Vespucci, 2016: Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting. Solar Energy, 134, 327-338, doi:10.1016/j.solener.2016.04.049.
Date:2016-05-18
Resource Type:article
Title:Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting
Abstract: 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. Keywords
Peer Review:Refereed
Copyright Information:Copyright 2016 Elsevier.
OpenSky citable URL: ark:/85065/d7tq635k
Publisher's Version: 10.1016/j.solener.2016.04.049
Author(s):
  • Federica Davò
  • Stefano Alessandrini - NCAR/UCAR
  • Simone Sperati
  • Luca Delle Monache - NCAR/UCAR
  • Davide Airoldi
  • Maria Vespucci
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