Title :
Method of short-term load forecasting based on BAYESIAN theorem
Author_Institution :
Autom. Dept., Jilin Vocational Coll. of Ind. & Technol., Jilin, China
Abstract :
Bayesian learning is a probability method that makes optimal decision based on known probability distribution and recently observed data. In the paper, by using the Bays estimate method, the weight of every forecasting model is obtained. Support Vector machines and Spectrum analysis are selected to construct the Bays combined model, which are applied to forecast. The forecasting method gives bigger weight to the models, which better conform to the variation of power load, and improves the precision. The sample calculation shows the combined model is better than those of the singular one.
Keywords :
load forecasting; statistical distributions; support vector machines; Bayesian learning; optimal decision; probability distribution; probability method; short-term load forecasting; spectrum analysis; support vector machines; Forecasting; Load forecasting; Load modeling; Predictive models; Spectral analysis; Support vector machines; Bays theorem; Spectrum analysis; Support Vector machines; combined forecasting;
Conference_Titel :
Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on
Conference_Location :
Jilin
Print_ISBN :
978-1-61284-719-1
DOI :
10.1109/MEC.2011.6025625
