Improved genetic algorithm-based research on optimization of least square support vector machines: an application of load forecasting
| العنوان: | Improved genetic algorithm-based research on optimization of least square support vector machines: an application of load forecasting |
|---|---|
| المؤلفون: | Zhang Xin-Yang, Li Hui, Lu Guang-Qian, Zhang Mei, Lin Bao-De |
| المصدر: | Soft Computing. 25:11997-12005 |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 0209 industrial biotechnology, Mathematical optimization, Computer science, Generalization, Load forecasting, Value (computer science), Computational intelligence, Sample (statistics), 02 engineering and technology, Theoretical Computer Science, Support vector machine, 020901 industrial engineering & automation, Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Geometry and Topology, State (computer science), Software |
| الوصف: | In this paper, the load forecasting model is established to increase the precision of meteorological impacts, temperature, short-term power load forecasting, working and holiday factors by considering the power load. Further, IGA-LS-SVM is proposed which is a short-term power load forecasting technique based on AI algorithm. And to increase the forecast accuracy and generalization capability of LS-SVM, we applied the adopted mutation probability and new coding technology to the parameter optimization of LS-SVM. The temperature, load, weather state, working and holiday days be taken as prediction model as input, and load value was predicted output. We selected the sample data from meteorological information and historical load of a city in Yunnan province. By results, the prediction verifies the good prediction effect when associated with existing BP algorithm and the proposed IGA- LS-SVM algorithm yields a value 0.8274 more significant than all others, which is appropriate for short-term power load prediction. |
| تدمد: | 1433-7479 1432-7643 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::b40bd215971d8eb595153a015e2c8c88 https://doi.org/10.1007/s00500-021-05674-9 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........b40bd215971d8eb595153a015e2c8c88 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 14337479 14327643 |
|---|