Load forecasting using elastic gradient descent
- Resource Type
- Conference
- Authors
- Hong, Yuan; Xia, Changhao; Zhang, Shixiang; Wu, Lin; Yuan, Chao; Huang, Ying; Wang, Xuxu; Zhu, Haifeng
- Source
- 2013 Ninth International Conference on Natural Computation (ICNC) Natural Computation (ICNC), 2013 Ninth International Conference on. :247-251 Jul, 2013
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Principal component analysis
Predictive models
Load modeling
Neural networks
Load forecasting
Forecasting
Training
elastic gradient descent method
time sequence
principal component analysis
load forecasting
error back propagation artificial neural network
- Language
- ISSN
- 2157-9555
2157-9563
The article describes in detail the theoretical basis of the elastic gradient descent method which combines the principal component analysis (PCA) and the time sequence method. In the short-term forecasting instance, the elastic gradient descent neural networks which combines the PCA and the time sequence method was used. The result verifies the effectiveness and feasibility of the introducing the PCA and the time sequence method in processing network optimization. The simulation result shows that this method has good prediction accuracy and convergence speed. In the long-term forecasting instance, the elastic gradient descent method which combines PCA method was used for that forecasting. The result indicated the superiority of the introducing the principal component analysis method in processing large amounts of data. As used herein, the model has good ductility and also lots of factors can be considered in. The prediction accuracy and generalization is good. And it will have a further application prospect in the actual forecast.