Short-Term Solar Radiation Forecasting by Using an Iterative Combination of Wavelet Artificial Neural Networks

Abstract

The information provided by accurate forecasts of solar energy time series are considered essential for performing an appropriate prediction of the electrical power that will be available in an electric system, as pointed out in Zhou et al. (2011). However, since the underlying data are highly non-stationary, it follows that to produce their accurate predictions is a very difficult assignment. In order to accomplish it, this paper proposes an iterative Combination of Wavelet Artificial Neural Networks (CWANN) which is aimed to produce shortterm solar radiation time series forecasting. Basically, the CWANN method can be split into three stages: at first one, a decomposition of level p, defined in terms of a wavelet basis, of a given solar radiation time series is performed, generating Wavelet Components (WC); at second one, these WCs are individually modeled by the k different ANNs, where , and the 5 best forecasts of each WC are combined by means of another ANN,

A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

“…It is critical to focus on types of cloud classification and forecasts to achieve effective solar irradiation forecasting. However, existing researchers often do not consider cloud conditions when developing their models, which eventually leads to poor forecast accuracy [60][61][62].…”