Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model
Abstract In recent years, photo‐voltaic (PV) system has become one of the most potential renewable energy power generation technologies because of its many advantages. Considering the influence of the randomness of PV system on the operation and dispatching of power system, the necessity of a compre...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-10-01
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| Series: | The Journal of Engineering |
| Online Access: | https://doi.org/10.1049/tje2.12186 |
| _version_ | 1797894276495966208 |
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| author | Tianze Liu Shusen Liu Shunjiang Wang Yanjuan Ma |
| author_facet | Tianze Liu Shusen Liu Shunjiang Wang Yanjuan Ma |
| author_sort | Tianze Liu |
| collection | DOAJ |
| description | Abstract In recent years, photo‐voltaic (PV) system has become one of the most potential renewable energy power generation technologies because of its many advantages. Considering the influence of the randomness of PV system on the operation and dispatching of power system, the necessity of a comprehensive forecasting model is increased rapidly. This paper proposes a short‐term PV power forecasting method based on MDCM‐GA‐LSTM model to solve the problem of low accuracy of traditional and single forecasting model. First, the meteorological data are pre‐processed based on wavelet denoising algorithm to improve the data quality. Second, a meteorological model is established based on genetic algorithm (GA) to optimize long short‐term memory (LSTM) neural network. Finally, the network is trained with the modified data of the meteorological model as the input and the actual power as the output, and the power forecasting model is established. In this study, the forecasting accuracy is evaluated based on mean absolute error (MAE) and root mean square error (RMSE). The experimental results demonstrated that the MDCM‐GA‐LSTM model outperforms the conventional and single model by a difference of about 98.43% of the MAE and 98.97% of the RMSE error. |
| first_indexed | 2024-04-10T07:06:25Z |
| format | Article |
| id | doaj.art-a432a6ee1d11480e9626376d13f55cea |
| institution | Directory Open Access Journal |
| issn | 2051-3305 |
| language | English |
| last_indexed | 2024-04-10T07:06:25Z |
| publishDate | 2022-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Engineering |
| spelling | doaj.art-a432a6ee1d11480e9626376d13f55cea2023-02-27T07:21:04ZengWileyThe Journal of Engineering2051-33052022-10-01202210994100510.1049/tje2.12186Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM modelTianze Liu0Shusen Liu1Shunjiang Wang2Yanjuan Ma3State Grid Panjin Electric Power Supply Company State Grid Liaoning Electric Power Supply Co., Ltd. Panjin ChinaState Grid Panjin Electric Power Supply Company State Grid Liaoning Electric Power Supply Co., Ltd. Panjin ChinaState Grid Liaoning Electric Power Supply Co., Ltd. Shenyang ChinaKey Laboratory of Regional Multi‐energy System Integration and Control of Liaoning Province Shenyang ChinaAbstract In recent years, photo‐voltaic (PV) system has become one of the most potential renewable energy power generation technologies because of its many advantages. Considering the influence of the randomness of PV system on the operation and dispatching of power system, the necessity of a comprehensive forecasting model is increased rapidly. This paper proposes a short‐term PV power forecasting method based on MDCM‐GA‐LSTM model to solve the problem of low accuracy of traditional and single forecasting model. First, the meteorological data are pre‐processed based on wavelet denoising algorithm to improve the data quality. Second, a meteorological model is established based on genetic algorithm (GA) to optimize long short‐term memory (LSTM) neural network. Finally, the network is trained with the modified data of the meteorological model as the input and the actual power as the output, and the power forecasting model is established. In this study, the forecasting accuracy is evaluated based on mean absolute error (MAE) and root mean square error (RMSE). The experimental results demonstrated that the MDCM‐GA‐LSTM model outperforms the conventional and single model by a difference of about 98.43% of the MAE and 98.97% of the RMSE error.https://doi.org/10.1049/tje2.12186 |
| spellingShingle | Tianze Liu Shusen Liu Shunjiang Wang Yanjuan Ma Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model The Journal of Engineering |
| title | Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model |
| title_full | Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model |
| title_fullStr | Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model |
| title_full_unstemmed | Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model |
| title_short | Short‐term photovoltaic power prediction based on MDCM‐GA‐LSTM model |
| title_sort | short term photovoltaic power prediction based on mdcm ga lstm model |
| url | https://doi.org/10.1049/tje2.12186 |
| work_keys_str_mv | AT tianzeliu shorttermphotovoltaicpowerpredictionbasedonmdcmgalstmmodel AT shusenliu shorttermphotovoltaicpowerpredictionbasedonmdcmgalstmmodel AT shunjiangwang shorttermphotovoltaicpowerpredictionbasedonmdcmgalstmmodel AT yanjuanma shorttermphotovoltaicpowerpredictionbasedonmdcmgalstmmodel |