Short-term photovoltaic power prediction on modal reconstruction: A novel hybrid model approach
[Display omitted] •A novel hybrid forecasting model for short-term PV power based on modal reconstruction is proposed.•An enhanced GWO algorithm is proposed.•The MAPE values of the proposed model are smaller than 3% under various weather conditions.•The proposed hybrid model accurately predicts the...
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| Vydáno v: | Sustainable energy technologies and assessments Ročník 45; s. 101048 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.06.2021
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| Témata: | |
| ISSN: | 2213-1388 |
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| Abstract | [Display omitted]
•A novel hybrid forecasting model for short-term PV power based on modal reconstruction is proposed.•An enhanced GWO algorithm is proposed.•The MAPE values of the proposed model are smaller than 3% under various weather conditions.•The proposed hybrid model accurately predicts the short-term PV power.•The proposed model improves the ability of power grid to absorb PV power.
The contribution is to propose a novel hybrid model based on modal reconstruction to predict short-term photovoltaic (PV) power. PV power generation large-scale grid connection causes the impact on the power system due to the instability and intermittence, and PV curtailment measures are taken to reduce the impact from voltage fluctuation. Accurate forecast is necessary to make a reasonable generation plan. A novel hybrid forecasting model is proposed, and an enhanced gray wolf optimization algorithm is proposed to improve the convergence ability and to solve the influence of extreme learning machine random parameters. The proposed algorithm has stronger convergence stability and higher convergence accuracy compared with the existing algorithms. The ensemble empirical mode decomposition algorithm is used to decompose the PV power under different weather conditions. The complexity of each component is calculated by the sample entropy, and the components are reconstructed to reduce the computational cost of forecasting models. The results revealed that mean absolute percentage error and root mean square error of the proposed model are smaller than 3% and 0.36 under various weather conditions. Meanwhile, the determination coefficient of the proposed model is more than 98%. |
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| AbstractList | [Display omitted]
•A novel hybrid forecasting model for short-term PV power based on modal reconstruction is proposed.•An enhanced GWO algorithm is proposed.•The MAPE values of the proposed model are smaller than 3% under various weather conditions.•The proposed hybrid model accurately predicts the short-term PV power.•The proposed model improves the ability of power grid to absorb PV power.
The contribution is to propose a novel hybrid model based on modal reconstruction to predict short-term photovoltaic (PV) power. PV power generation large-scale grid connection causes the impact on the power system due to the instability and intermittence, and PV curtailment measures are taken to reduce the impact from voltage fluctuation. Accurate forecast is necessary to make a reasonable generation plan. A novel hybrid forecasting model is proposed, and an enhanced gray wolf optimization algorithm is proposed to improve the convergence ability and to solve the influence of extreme learning machine random parameters. The proposed algorithm has stronger convergence stability and higher convergence accuracy compared with the existing algorithms. The ensemble empirical mode decomposition algorithm is used to decompose the PV power under different weather conditions. The complexity of each component is calculated by the sample entropy, and the components are reconstructed to reduce the computational cost of forecasting models. The results revealed that mean absolute percentage error and root mean square error of the proposed model are smaller than 3% and 0.36 under various weather conditions. Meanwhile, the determination coefficient of the proposed model is more than 98%. |
| ArticleNumber | 101048 |
| Author | Liu, Han-Min Hashan Md Mashud, Abu Liu, Zhi-Feng Tseng, Ming-Lang Luo, Shi-Fan Li, Lingling |
| Author_xml | – sequence: 1 givenname: Zhi-Feng surname: Liu fullname: Liu, Zhi-Feng email: tjliuzhifeng@126.com organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China – sequence: 2 givenname: Shi-Fan surname: Luo fullname: Luo, Shi-Fan email: luo.shif@northeastern.edu organization: College of Engineering, Northeastern University, Boston 02115, USA – sequence: 3 givenname: Ming-Lang surname: Tseng fullname: Tseng, Ming-Lang email: tsengminglang@asia.edu.tw, tsengminglang@gmail.com organization: Institute of Innovation and Circular Economy, Asia University, Taiwan – sequence: 4 givenname: Han-Min surname: Liu fullname: Liu, Han-Min email: liu199920002001@163.com organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China – sequence: 5 givenname: Lingling surname: Li fullname: Li, Lingling email: lilinglinglaoshi@126.com organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China – sequence: 6 givenname: Abu surname: Hashan Md Mashud fullname: Hashan Md Mashud, Abu email: mashud@hstu.ac.bd organization: Department of Mathematics, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh |
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| Keywords | Photovoltaic power forecasting Gray wolf optimization algorithm Ensemble empirical mode decomposition Extreme learning machine Modal reconstruction |
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•A novel hybrid forecasting model for short-term PV power based on modal reconstruction is proposed.•An enhanced GWO algorithm is... |
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| SubjectTerms | Ensemble empirical mode decomposition Extreme learning machine Gray wolf optimization algorithm Modal reconstruction Photovoltaic power forecasting |
| Title | Short-term photovoltaic power prediction on modal reconstruction: A novel hybrid model approach |
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