Title :
Application of self-organizing combination forecasting method in power load forecast
Author :
Sun, Wei ; Zhang, Xing
Author_Institution :
North China Electr. Power Univ., Baoding
Abstract :
According to the load properties of electric power, four kinds of component forecasting models are chosen and a new combination forecasting model based on Self-organizing data mining algorithm is introducted in this paper. The forecasted results of each component forcasting models are used as the input of self-organizing data mining algorithm, and the output are the results of combination forecasting. In order to vertify the validity and maneuverability of the model, a load forecasting example is given and the result show that this model can improve the forecasting ability remarkably when comparing to optimal combination forecasting and artificial neural network combination forecasting.
Keywords :
data mining; load forecasting; power engineering computing; self-organising feature maps; combination forecasting; component forecasting; electric power; power load forecasting; self-organizing data mining; Artificial neural networks; Data mining; Economic forecasting; Energy management; Load forecasting; Polynomials; Power system modeling; Predictive models; Sun; Testing; Power load forecast; Self-organizing combination forecasting; Self-organizing data mining algorithm;
Conference_Titel :
Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-1065-1
Electronic_ISBN :
978-1-4244-1066-8
DOI :
10.1109/ICWAPR.2007.4420742
