Improved Electricity Portfolio Prediction Based on Optimized Ant Colony Algorithm
In order to overcome the shortcomings of the traditional single power forecasting method, the article uses LSTM network, GM model and SVR support vector machine regression model to forecast electricity, and also uses ant colony optimization algorithm to build a new combined forecasting model for the...
| Main Author: | |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2023-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/426012 |
| _version_ | 1797206556392030208 |
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| author | Li Zemin |
| author_facet | Li Zemin |
| author_sort | Li Zemin |
| collection | DOAJ |
| description | In order to overcome the shortcomings of the traditional single power forecasting method, the article uses LSTM network, GM model and SVR support vector machine regression model to forecast electricity, and also uses ant colony optimization algorithm to build a new combined forecasting model for the three forecasting methods, which takes into account the factors affecting power forecasting more comprehensively and helps to improve the accuracy of power forecasting. The paper also uses the ant colony algorithm to optimize the weights of the single forecasting method, which can effectively avoid the problem of the traditional algorithm falling into the local optimal point, and obtain a more accurate power combination forecasting model. Through application examples, it is verified that the combined forecasting model can effectively improve the accuracy of power forecasting and provide reference for power system planning and operation. The research results show that the combined prediction has a greater improvement in accuracy compared with the single Gray, LSTM network and other predictions. |
| first_indexed | 2024-04-24T09:08:54Z |
| format | Article |
| id | doaj.art-ebaf7b6355114eb1b55fac789c39d1c0 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:08:54Z |
| publishDate | 2023-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-ebaf7b6355114eb1b55fac789c39d1c02024-04-15T18:17:44ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392023-01-0130245846410.17559/TV-20221007130139Improved Electricity Portfolio Prediction Based on Optimized Ant Colony AlgorithmLi Zemin0Inner Mongolia Power (Group) Company Limited Electricity Marketing Service and Operation Management BranchIn order to overcome the shortcomings of the traditional single power forecasting method, the article uses LSTM network, GM model and SVR support vector machine regression model to forecast electricity, and also uses ant colony optimization algorithm to build a new combined forecasting model for the three forecasting methods, which takes into account the factors affecting power forecasting more comprehensively and helps to improve the accuracy of power forecasting. The paper also uses the ant colony algorithm to optimize the weights of the single forecasting method, which can effectively avoid the problem of the traditional algorithm falling into the local optimal point, and obtain a more accurate power combination forecasting model. Through application examples, it is verified that the combined forecasting model can effectively improve the accuracy of power forecasting and provide reference for power system planning and operation. The research results show that the combined prediction has a greater improvement in accuracy compared with the single Gray, LSTM network and other predictions.https://hrcak.srce.hr/file/426012combinatorial predictiongray predictionneural networkssupport vector machines |
| spellingShingle | Li Zemin Improved Electricity Portfolio Prediction Based on Optimized Ant Colony Algorithm Tehnički Vjesnik combinatorial prediction gray prediction neural networks support vector machines |
| title | Improved Electricity Portfolio Prediction Based on Optimized Ant Colony Algorithm |
| title_full | Improved Electricity Portfolio Prediction Based on Optimized Ant Colony Algorithm |
| title_fullStr | Improved Electricity Portfolio Prediction Based on Optimized Ant Colony Algorithm |
| title_full_unstemmed | Improved Electricity Portfolio Prediction Based on Optimized Ant Colony Algorithm |
| title_short | Improved Electricity Portfolio Prediction Based on Optimized Ant Colony Algorithm |
| title_sort | improved electricity portfolio prediction based on optimized ant colony algorithm |
| topic | combinatorial prediction gray prediction neural networks support vector machines |
| url | https://hrcak.srce.hr/file/426012 |
| work_keys_str_mv | AT lizemin improvedelectricityportfoliopredictionbasedonoptimizedantcolonyalgorithm |