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

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. Moreover we found that our data preprocessing approach can reduce significantly forecasting errors. © 2009 Springer Berlin Heidelberg.