Hybrid Solar Forecasting Method Based on Empirical Mode Decomposition and Back Propagation Neural Network

Ahmed Aghmadi,Nisrin Naseri,Soumia El Hani,Fatima El Issaoui,Hamza Mediouni,O.P Malik

doi:10.1051/e3sconf/202123102001

Abstract

In order to improve the accuracy of solar radiation prediction and optimize the energy management system. This study proposes a forecasting model based on empirical mode decomposition (EMD) and Back Propagation Neural Network (BPNN). Empirical mode of decomposition (EMD)-based ensemble methods with powerful predictive abilities have become relatively common in forecasting study. First, the existing solar radiation datasets are decomposed into an intrinsic mode function (IMF) and one residue produces fairly stationary sub-series that can easily be modeled on BPNN. Next, both components of the IMF and residue are applied to create the respective BPNN models. Then, the corresponding BPNN is used to predict some sub-series. Finally, the predictive values of the original solar radiation datasets are determined by the sum of each predicted sub-series. Compared with traditional models such as conventional neural network or ARIMA time series, the hybrid EMD-BPNN model shows great results in term of RMSE with 28.13 (W/m2). On the other hand, the result of BPNN and ARIMA was 83.28 (W/m2) and 108.88 (W/m2), respectively. that the non-stationary and non-linear of solar radiation signal has less effect on the accuracy of the prediction.

Highlights

Climatologists are increasingly convinced that climate change has been one of the biggest threats in the world over the last few decades [1]
PV power generation is challenging as it is strongly linked to numerous environmental variables, such as solar irradiance, temperature, wind speed and cloud movement [3]
Multiple studies have been published on methods for forecasting solar radiation, which can be categorized in three groups: (a) physical models; (b) statistical models; (c) ensemble methods

Summary

Introduction

Climatologists are increasingly convinced that climate change has been one of the biggest threats in the world over the last few decades [1]. Renew- able resources including wind and solar energy, ought to be utilized effectively to overcome the rapidly expanding in energy needs and changing climate [2]. Multiple studies have been published on methods for forecasting solar radiation, which can be categorized in three groups: (a) physical models; (b) statistical models; (c) ensemble methods. Solar radiation prediction models can be developed using physical parameters such as temperature, pressure, and Sky Imagery for longer-term forecasts.

Methods

Results

Conclusion