Use of exogenous data to improve an artificial neural network dedicated to daily global radiation forecasting

Abstract : This paper presents an application of Artificial Neural Networks (ANNs) in the renewable energy domain and, more particularly, to predict solar energy. We look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. In previous studies, we have demonstrated that an optimized ANN with endogenous inputs can forecast the solar radiation on a horizontal surface with acceptable errors. Thus we propose to study the contribution of exogenous meteorological data to our optimized MLP and compare with different forecasting methods used previously: a naïve forecaster like persistence and an ANN with preprocessing using only endogenous inputs. Although intuitively the use of meteorological data may increase the quality of prediction, the obtained results are relatively mixed. The use of exogenous data generates a decrease of nRMSE between 0.5% and 1% for the two studied locations. The absolute error (RMSE) is decreased by 52 Wh/m²/day in the simple endogenous case and 335 Wh/m²/day for the persistence forecast.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00485887
Contributor : Christophe Paoli <>
Submitted on : Saturday, May 22, 2010 - 9:19:58 AM
Last modification on : Thursday, January 11, 2018 - 6:16:28 AM

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Christophe Paoli, Cyril Voyant, Marc Muselli, Marie Laure Nivet. Use of exogenous data to improve an artificial neural network dedicated to daily global radiation forecasting. International Conference on Environment and Electrical Engineering, May 2010, Prague, Czech Republic. pp.49-52. ⟨hal-00485887⟩

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