Solar radiation forecasting using ad-hoc time series preprocessing and neural networks

Christophe Paoli; Cyril Voyant; Marc Muselli; Marie-Laure Nivet

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arXiv · 0906.0311

Solar radiation forecasting using ad-hoc time series preprocessing and neural networks

Christophe Paoli·Cyril Voyant·Marc Muselli·Marie-Laure Nivet

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Abstract

In this paper, we present an application of neural networks in the renewable energy domain. We have developed a methodology for the daily prediction of global solar radiation on a horizontal surface. We use an ad-hoc time series preprocessing and a Multi-Layer Perceptron (MLP) in order to predict solar radiation at daily horizon. First results are promising with nRMSE < 21% and RMSE < 998 Wh/m2. Our optimized MLP presents prediction similar to or even better than conventional methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors approximators. Moreover we found that our data preprocessing approach can reduce significantly forecasting errors.

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Christophe Paoli, Cyril Voyant, Marc Muselli, Marie-Laure Nivet. 2009-06-01. Solar radiation forecasting using ad-hoc time series preprocessing and neural networks. https://arxiv.org/abs/0906.0311

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