Research of Residual Error-Ant Colony Optimization Gray Model Based on Markov in Load Forecasting

GM(1,1) forecasting model has the advantages of few sample data required, easy calculation, high prediction accuracy in short terms, examination, etc. it is extensively applied in the load forecasting. However, it has its localization. The greater the gray level of data is greater, the lower the pre...

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Bibliographic Details
Main Authors: Niu Dongxiao, Li Yanchang, Li Wei
Format: Conference Proceeding
Language:English
Subjects:
ACO
Analytical models
Biological system modeling
Cities and towns
Equal dimension new information
Forecasting
Markov chain
Mathematical model
Power systems
Predictive models
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Summary:GM(1,1) forecasting model has the advantages of few sample data required, easy calculation, high prediction accuracy in short terms, examination, etc. it is extensively applied in the load forecasting. However, it has its localization. The greater the gray level of data is greater, the lower the prediction precision is. Besides, it is not suitable for long term forecasting of economy to step backwards for years, which, to a certain extent, is caused by parameter a in the model. To solve the problem, vector thetas was introduced to set up residual error GM(1,1, thetas) model, which is solved by ant colony optimization (ACO). Meanwhile equal dimension new information and Markov matrix are applied to estimate symbol of residual error forecast value when k > n. Case analysis shows that it effectively improves prediction precision in comparison with traditional forecasting methods. Application shows that the method has definite utility value.
ISSN:2154-9613
DOI:10.1109/CCCM.2008.179