Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting

TL;DR

This paper introduces a novel wavelet recurrent neural network approach for solar radiation forecasting that operates in the wavelet domain, effectively capturing meteorological data variations for accurate 2-day predictions.

Contribution

It presents a new WRNN model that predicts solar radiation in the wavelet domain and performs inverse transforms, improving accuracy over existing hybrid neural network methods.

Findings

Achieved very low root-mean-square error in predictions

Effective exploitation of meteorological data correlations

Outperformed recent hybrid neural network approaches

Abstract

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.