Short term power load forecasting system based on improved neural network deep learning model
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Wiley Open Access
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| Natura: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867011372719538176 |
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| author | Lulu Yuan |
| author_facet | Lulu Yuan Lulu Yuan |
| collection | Wiley Open Access |
| contents | Short term power load forecasting system based on improved neural network deep learning model Lulu Yuan Advanced Control for Applications AbstractThe electricity load prediction is closely related to production and daily life. The electricity load prediction is also a very important task. With the widespread application of smart grids, load data shows an exponential growth trend. The huge amount of data in the load makes power prediction even more difficult. On the basis of traditional prediction algorithms, a power load prediction model based on machine learning and neural networks is designed. Because the single model prediction has the unstable results, a combined model is obtained based on the ensemble learning idea and two single model prediction method. The prediction results are detected by the load data. From the experimental results, the mean absolute percentage error (MAPE) of the AdaBoost‐GRU data fusion model is 0.066%. Compared to the AdaBoost‐GRU data fusion model, the MAPE decreases by 1.59% and 1.12%, respectively. The relative mass scores of the two groups decrease by 132.57% and 89.14%, respectively. The prediction accuracy is improved, which has advantages compared to traditional combination models. It can effectively enhance the accuracy of short‐term power grid load forecasting. It is an important scientific and practical reference for power grid decision‐making. 10.1002/adc2.197 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/adc2.197 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_adc2_197 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Short term power load forecasting system based on improved neural network deep learning model Lulu Yuan Advanced Control for Applications Short term power load forecasting system based on improved neural network deep learning model Lulu Yuan Advanced Control for Applications AbstractThe electricity load prediction is closely related to production and daily life. The electricity load prediction is also a very important task. With the widespread application of smart grids, load data shows an exponential growth trend. The huge amount of data in the load makes power prediction even more difficult. On the basis of traditional prediction algorithms, a power load prediction model based on machine learning and neural networks is designed. Because the single model prediction has the unstable results, a combined model is obtained based on the ensemble learning idea and two single model prediction method. The prediction results are detected by the load data. From the experimental results, the mean absolute percentage error (MAPE) of the AdaBoost‐GRU data fusion model is 0.066%. Compared to the AdaBoost‐GRU data fusion model, the MAPE decreases by 1.59% and 1.12%, respectively. The relative mass scores of the two groups decrease by 132.57% and 89.14%, respectively. The prediction accuracy is improved, which has advantages compared to traditional combination models. It can effectively enhance the accuracy of short‐term power grid load forecasting. It is an important scientific and practical reference for power grid decision‐making. 10.1002/adc2.197 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Short term power load forecasting system based on improved neural network deep learning model |
| topic | Advanced Control for Applications |
| url | https://onlinelibrary.wiley.com/doi/10.1002/adc2.197 |