Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6685857 | Applied Energy | 2015 | 16 Pages |
Abstract
The AnEn performance for SPF is compared to a quantile regression (QR) technique and a persistence ensemble (PeEn) over three solar farms in Italy spanning different climatic conditions. The QR is a state-of-the-science method for probabilistic predictions that, similarly to AnEn, is based on a historical data set. The PeEn is a persistence model for probabilistic predictions, where the most recent 20 power measurements available at the same lead-time are used to form an ensemble. The performance assessment has been carried out evaluating important attributes of a probabilistic system such as statistical consistency, reliability, resolution and skill. The AnEn performs as well as QR for common events, by providing predictions with similar reliability, resolution and sharpness, while it exhibits more skill for rare events and during hours with a low solar elevation.
Keywords
SPFRegional Atmospheric Modeling SystemContinuous ranked probability scoreAzimuth angleAnalog ensemblemean powerBrier Skill ScorewpfMREBSSNWPCRPSRMSERAMsBrier scoreMAEEnsemble verificationGlobal horizontal irradianceProbability density functionQuantile regressionroot mean squared errorNeural networkGHIPhotovoltaic powerMean Absolute ErrorCloud coverPdfNumerical weather predictionSolar power forecastingUncertainty quantification
Related Topics
Physical Sciences and Engineering
Energy
Energy Engineering and Power Technology
Authors
S. Alessandrini, L. Delle Monache, S. Sperati, G. Cervone,