Title :
A long-term electrical power load forecasting model based on grey feed-back modification
Author_Institution :
Dept. of Econ. & Manage., North China Electr. Power Univ., Baoding
Abstract :
There are many uncertain factors for electricity consumption with the characteristic of obvious changing tendency. The traditional grey model has been widely used in the field of forecasting systems. However, many results have shown that the model is biased. On the basis of the biased model, this paper introduces a new grey feed-back modification model, LGM(1.1), for long-term forecasting. Using this model, The trial forecasting precision of electric power load in certain region of Hebei province is improved.
Keywords :
feedback; grey systems; load forecasting; power consumption; biased model; electricity consumption; forecasting systems; grey feedback modification; grey model; long-term electrical power load forecasting model; uncertain factors; Cybernetics; Economic forecasting; Information systems; Load forecasting; Load modeling; Machine learning; Power generation economics; Power system economics; Power system modeling; Predictive models; LGM(1,1); grey feed-back modification; long-term forecasting model; power load;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620770
