Survey of grid resource monitoring and prediction strategies.
- Published version (with publisher's formatting)
This literature focuses on grid resource monitoring and prediction, representative monitoring and prediction systems are analyzed and evaluated, then monitoring and prediction strategies for grid resources are summarized and discussed, recommendations are also given for building monitoring sensors and prediction models. During problem definition, one-step-ahead prediction is extended to multi-step-ahead prediction, which is then modeled with computational intelligence algorithms such as neural network and support vector regression. Numerical simulations are performed on benchmark data sets, while comparative results on accuracy and efficiency indicate that support vector regression models achieve superior performance. Our efforts can be utilized as direction for building online monitoring and prediction system for grid resources.
|Research Areas:||A. > School of Science and Technology > Computer and Communications Engineering|
A. > School of Science and Technology > Computer Science > Artificial Intelligence group
|Notes on copyright:||We acknowledge permission from IJIIP for hosting published work. (5/11)|
|Deposited On:||26 Apr 2011 15:26|
|Last Modified:||02 May 2015 20:56|
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