Solar Radiation Forecasting Using Ad-Hoc Time Series Preprocessing and Neural Networks

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.

• Type: Conference
• Year: 2009
• Where: ICIC
• Authors: Christophe Paoli, Cyril Voyant, Marc Muselli, Marie-Laure Nivet
• Keywords: Applied Computing, Global Solar Radiation, ICIC 2009, MLP Presents Prediction, Solar Radiation