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Atmosphere 2016, 7(2), 18;

Solar Forecasting in a Challenging Insular Context

PIMENT laboratory, University of La Réunion, 15 avenue René Cassin, 97715 Saint-Denis, France
Department of Physics, University of Oldenburg, Carl-von-Ossietzky-Street 9-11, 26129 Oldenburg, Germany
Author to whom correspondence should be addressed.
Academic Editor: John Boland
Received: 15 December 2015 / Revised: 18 January 2016 / Accepted: 22 January 2016 / Published: 29 January 2016
(This article belongs to the Special Issue Climate Variable Forecasting)
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This paper aims at assessing the accuracy of different solar forecasting methods in the case of an insular context. Two sites of La Réunion Island, Le Tampon and Saint-Pierre, are chosen to do the benchmarking exercise. Réunion Island is a tropical island with a complex orography where cloud processes are mainly governed by local dynamics. As a consequence, Réunion Island exhibits numerous micro-climates. The two aforementioned sites are quite representative of the challenging character of solar forecasting in the case of a tropical island with complex orography. Hence, although distant from only 10 km, these two sites exhibit very different sky conditions. This work focuses on day-ahead and intra-day solar forecasting. Day-ahead solar forecasts are provided by the European Center for Medium-Range Weather Forecast (ECMWF). This organization maintains and runs the Numerical Weather Prediction (NWP) model named Integrated Forecast System (IFS). In this work, post-processing techniques are applied to refine the output of the IFS model for day-ahead forecasting. Statistical models like a recursive linear model or a nonlinear model such as an artificial neural network are used to produce the intra-day solar forecasts. It is shown that a combination of the IFS model and the neural network model further improves the accuracy of the forecasts. View Full-Text
Keywords: solar forecasting; statistical models; NWP model; model output statistics solar forecasting; statistical models; NWP model; model output statistics

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Lauret, P.; Lorenz, E.; David, M. Solar Forecasting in a Challenging Insular Context. Atmosphere 2016, 7, 18.

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