| Title |
Mode decomposition-based short-term multi-step hybrid solar forecasting model for microgrid applications |
| ID_Doc |
66891 |
| Authors |
Nahid, FA; Ongsakul, W; Manjiparambil, NM; Singh, JG; Roy, J |
| Title |
Mode decomposition-based short-term multi-step hybrid solar forecasting model for microgrid applications |
| Year |
2024 |
| Published |
Electrical Engineering, 106, 3 |
| DOI |
10.1007/s00202-023-02138-1 |
| Abstract |
A sustainable energy sector and achieving carbon neutrality in microgrids require a firm commitment to renewable energy resources. A sharp focus on solar energy holds the most promising potential for a low-carbon energy pathway. Efficient and optimal energy management application in the case of such microgrid systems requires the development of an accurate forecasting technique. A significant obstacle in creating such an accurate prediction tool is the intermittent nature of solar energy. To address this challenge, this research proposes a novel threefold hybrid model that integrates empirical mode decomposition (EMD), convolutional neural networks (CNN), and long short-term memory (LSTM) neural networks to forecast solar radiation up to three steps ahead. We do this utilizing two distinct solar radiation datasets obtained from two different sites in Thailand: one at 30-min intervals and the second one at 15-min intervals. To assess the forecasting capabilities of the proposed model, this study has carried out a comprehensive analysis by comparing it to six alternative models considering hybrid and stand-alone options. The proposed model outperformed them all, establishing its supremacy with the lowest mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE), additionally, ensuring that it meets the industry standards for forecasting applications. The proposed model's effectiveness in accurately predicting solar radiation has been successfully tested for a microgrid case study site in Thailand which supplies power to a business complex and uses the innovative Green Energy Management System (GEMS) developed by Leonics Co. Ltd. |
| Author Keywords |
Convolutional long short-term memory; Empirical mode decomposition; Short-term forecast; Solar radiation forecast; Microgrid energy management |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001126302100001 |
| WoS Category |
Engineering, Electrical & Electronic |
| Research Area |
Engineering |
| PDF |
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