Wednesday, August 16th, 2017

Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation

Publication year: 2010brbSource:/b Energy, In Press, Corrected Proof, Available online 23 November 2010brCyril, Voyant , Marc, Muselli , Christophe, Paoli , Marie-Laure, NivetbrThis paper presents an application of Artificial Neural Networks (ANNs) to predict daily solar radiation. We look at the Multi-Layer Perceptron (MLP) network which is the most used of ANNs architectures. In previous studies, we have developed an ad-hoc time series preprocessing and optimized a MLP with endogenous inputs in order to forecast the solar radiation on a horizontal surface. We propose in this paper to study the contribution of exogenous meteorological data (multivariate method) as time series to our optimized MLP and compare with different forecasting methods: a naïve forecaster (persistence), ARIMA reference predictor, an ANN with preprocessing using…br Research highlights: ► Use of exogenous data as ANN inputs to forecast horizontal daily global irradiation data. ► New methodology allowing to choice the adequate exogenous data – a systematic method comparing endogenous and exogenous data. ► Different referenced mathematical predictors allows to conclude about the pertinence of the proposed methodology.

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