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- Title
Improved Electricity Portfolio Prediction Based on Optimized Ant Colony Algorithm.
- Authors
Li ZEMIN
- Abstract
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.
- Subjects
ANT algorithms; ANTS; LOAD forecasting (Electric power systems); GRAY codes; SUPPORT vector machines
- Publication
Technical Gazette / Tehnički Vjesnik, 2023, Vol 30, Issue 2, p458
- ISSN
1330-3651
- Publication type
Article
- DOI
10.17559/TV-20221007130139