Issue |
E3S Web Conf.
Volume 231, 2021
2020 2nd International Conference on Power, Energy and Electrical Engineering (PEEE 2020)
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Article Number | 02001 | |
Number of page(s) | 5 | |
Section | Renewable Energy System and Engineering | |
DOI | https://doi.org/10.1051/e3sconf/202123102001 | |
Published online | 25 January 2021 |
Hybrid Solar Forecasting Method Based on Empirical Mode Decomposition and Back Propagation Neural Network
Energy optimization, Diagnosis and Control, STIS Center, ENSET, Mohamed V University, Rabat, Morocco
* Corresponding author: ahmed.aghmadi@gmail.com
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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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