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Energies 2015, 8(9), 9594-9619; doi:10.3390/en8099594

The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

1
Sustainability & Energy Sources, Ricerca Sistema Energetico (RSE) SpA, Milano 20134, Italy
2
Research Applications Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80301, USA
3
Electrical Engineering, Technical University of Denmark (DTU), Kgs. Lyngby 2800, Denmark
4
Renewable Energies & SmartGrids, MINES ParisTech, Sophia Antipolis 06904, France
*
Author to whom correspondence should be addressed.
Academic Editor: Guido Carpinelli
Received: 23 June 2015 / Revised: 7 August 2015 / Accepted: 20 August 2015 / Published: 3 September 2015
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Abstract

A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future. View Full-Text
Keywords: short-term energy forecasting; wind power; solar power; renewable energies; probabilistic forecasting; meteorological modeling; benchmarking comparison short-term energy forecasting; wind power; solar power; renewable energies; probabilistic forecasting; meteorological modeling; benchmarking comparison
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sperati, S.; Alessandrini, S.; Pinson, P.; Kariniotakis, G. The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation. Energies 2015, 8, 9594-9619.

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