A novel application of an analog ensemble for short-term wind power forecasting

S. Alessandrini, L. Delle Monache, S. Sperati and J.N. Nissen

Renewable Energy, 2015, vol. 76, issue C, 768-781

Abstract: The efficient integration of wind in the energy market is limited by its natural variability and predictability. This limitation can be tackled by using the probabilistic predictions that provide accurate deterministic forecasts along with a quantification of their uncertainty. We propose as a novelty the application of an analog ensemble (AnEn) method to generate probabilistic wind power forecasts (WPF). The AnEn prediction of a given variable is constituted by a set of measurements of the past, concurrent to the past forecasts most similar to the current one. The AnEn performance for WPF is compared with three state-of-the-science methods for probabilistic predictions over a wind farm and a 505-day long period: a wind power prediction based on the ensemble wind forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (ECMWF-EPS), the Limited-area Ensemble Prediction System (LEPS) developed within the COnsortium for Small-scale MOdelling (COSMO-LEPS) and a quantile regression (QR) technique. The AnEn performs as well as ECMWF-EPS, COSMO-LEPS and QR for common events while it exhibits more skill for rare events. A comparison with the performances obtained with a deterministic forecasting method based on a Neural Network is also carried out showing the benefits of using AnEn.

Keywords: Analog ensemble; Short-term wind power forecasting; Probabilistic predictions; Uncertainty quantification; Ensemble verification (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (32)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148114007915
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:76:y:2015:i:c:p:768-781

DOI: 10.1016/j.renene.2014.11.061

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().