Title :
Research of short-term load forecasting based on combined grey neural network and phase space reconstruction
Author :
Wang, Shuo-he ; Hao, Rui-lin ; Chang, Yu-jian ; Zhao, Yao
Author_Institution :
Dept. of Electr. & Electron Eng., Shijiazhuang Railway Inst., Shijiazhuang, China
Abstract :
According to the characteristics of grey theory, G-P algorithm of phase space reconstruction and artificial neural network (ANN), a combined algorithm (G-G-NN) is proposed. The original time series is transformed by accumulated generating of grey prediction and G-P algorithm of phase space reconstruction. When a regular time series phase space is generated, neural network is adopted to forecast. The practical example indicated that the algorithm is verified.
Keywords :
grey systems; load forecasting; neural nets; power system analysis computing; G-P algorithm; artificial neural network; grey neural network; grey theory; phase space reconstruction; short-term load forecasting; Artificial neural networks; Clustering algorithms; Cybernetics; Load forecasting; Machine learning; Neural networks; Power system modeling; Prediction algorithms; Predictive models; Steel; Electric power systems; G-G-NN algorithm; G-P algorithm; Grey theory; Neural network; Short-term load forecasting;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212444
