Combining solar irradiance measurements,satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting |
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| Affiliation: | 1. University Institute for Intelligent Systems and Numerical Applications in Engineering, University of Las Palmas de Gran Canaria, Edificio Central del Parque Tecnológico, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain;2. Laboratoire de Physique et Ingénierie Mathématique pour l’Energie et l’environnement (PIMENT), University of La Réunion, Campus du Moufia 15, Avenue René Cassin, 97715 Saint Denis Messag 9, France;1. Electrical Engineering Department, College of Engineering and Petroleum, Kuwait University, Kuwait;2. Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt;1. UFMA – Electrical Energy Institute, Federal University of Maranhão, São Luís, MA, Brazil;2. INESC TEC – Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal;3. UP – University of Porto, Faculty of Engineering, Portugal;1. Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul, Republic of Korea;2. Research Institute of Marine Systems Engineering, Seoul National University, Seoul, Republic of Korea;3. Hanjin Shipping Co., Ltd., Busan, Republic of Korea |
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| Abstract: | Isolated power systems need to generate all the electricity demand with their own renewable resources. Among the latter, solar energy may account for a large share. However, solar energy is a fluctuating source and the island power grid could present an unstable behavior with a high solar penetration. Global Horizontal Solar Irradiance (GHI) forecasting is an important issue to increase solar energy production into electric power system. This study is focused in hourly GHI forecasting from 1 to 6 h ahead. Several statistical models have been successfully tested in GHI forecasting, such us autoregressive (AR), autoregressive moving average (ARMA) and Artificial Neural Networks (ANN). In this paper, ANN models are designed to produce intra-day solar forecasts using ground and exogenous data. Ground data were obtained from two measurement stations in Gran Canaria Island. In order to improve the results obtained with ground data, satellite GHI data (from Helioclim-3) as well as solar radiation and Total Cloud Cover forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as additional inputs of the ANN model. It is shown that combining exogenous data (satellite and ECMWF forecasts) with ground data further improves the accuracy of the intra-day forecasts. |
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| Keywords: | Solar forecasting Numerical weather prediction Artificial neural networks Satellite images |
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