Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory
To solve the problem of photovoltaic power prediction in areas with large climate changes, this article proposes a hybrid Long Short-Term Memory method to improve the prediction accuracy and noise resistance. It combines the improved complete ensemble empirical mode decomposition with adaptive noise...
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6365 |
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| author | Lan Cao Haoyu Yang Chenggong Zhou Shaochi Wang Yingang Shen Binxia Yuan |
| author_facet | Lan Cao Haoyu Yang Chenggong Zhou Shaochi Wang Yingang Shen Binxia Yuan |
| author_sort | Lan Cao |
| collection | DOAJ |
| description | To solve the problem of photovoltaic power prediction in areas with large climate changes, this article proposes a hybrid Long Short-Term Memory method to improve the prediction accuracy and noise resistance. It combines the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and kernel principal component analysis (KPCA) algorithm. The ICEEMDAN algorithm reduces the instability of the environmental factor sequence. The KPCA algorithm reduces the input dimensions of the model. LSTM performs dynamic time modeling of the multivariate feature sequences to predict the output PV power. The adaptability of the ICEEMDAN-KPCA-LSTM model is assessed with datasets from a PV plant in west China and evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared metrics. Using 70% of the datasets for output PV power estimation, the results show a good performance, with an RMSE of 4.3715, MAPE of 8.9264%, and R-squared value of 89.973%. By comparing with other prediction models, the ICEEMDAN-KPCA-LSTM photovoltaic output power model outperforms other models. |
| format | Article |
| id | doaj-art-8b2ddbcbc0c5495d9d6c450a5a5ced10 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-8b2ddbcbc0c5495d9d6c450a5a5ced102025-08-20T02:53:29ZengMDPI AGEnergies1996-10732024-12-011724636510.3390/en17246365Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term MemoryLan Cao0Haoyu Yang1Chenggong Zhou2Shaochi Wang3Yingang Shen4Binxia Yuan5College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaTo solve the problem of photovoltaic power prediction in areas with large climate changes, this article proposes a hybrid Long Short-Term Memory method to improve the prediction accuracy and noise resistance. It combines the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and kernel principal component analysis (KPCA) algorithm. The ICEEMDAN algorithm reduces the instability of the environmental factor sequence. The KPCA algorithm reduces the input dimensions of the model. LSTM performs dynamic time modeling of the multivariate feature sequences to predict the output PV power. The adaptability of the ICEEMDAN-KPCA-LSTM model is assessed with datasets from a PV plant in west China and evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared metrics. Using 70% of the datasets for output PV power estimation, the results show a good performance, with an RMSE of 4.3715, MAPE of 8.9264%, and R-squared value of 89.973%. By comparing with other prediction models, the ICEEMDAN-KPCA-LSTM photovoltaic output power model outperforms other models.https://www.mdpi.com/1996-1073/17/24/6365photovoltaic output power predictionshort-term forecastingICEEMDANKPCALSTM |
| spellingShingle | Lan Cao Haoyu Yang Chenggong Zhou Shaochi Wang Yingang Shen Binxia Yuan Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory Energies photovoltaic output power prediction short-term forecasting ICEEMDAN KPCA LSTM |
| title | Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory |
| title_full | Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory |
| title_fullStr | Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory |
| title_full_unstemmed | Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory |
| title_short | Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory |
| title_sort | photovoltaic short term output power forecast model based on improved complete ensemble empirical mode decomposition with adaptive noise kernel principal component analysis long short term memory |
| topic | photovoltaic output power prediction short-term forecasting ICEEMDAN KPCA LSTM |
| url | https://www.mdpi.com/1996-1073/17/24/6365 |
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