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http://hdl.handle.net/2080/764
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| Title: | Short Term Load Forecasting using a Neural Network trained by A Hybrid Artificial Immune System |
| Authors: | Mishra, Sanjib
Patra, S K |
| Keywords: | Short term load forecasting,
genetic algortithm
particle swarm optimization
Aritificial immune system |
| Issue Date: | 2008 |
| Citation: | Proceedings of IEEE Region 10 Colloquium and the Third International Conference on Industrial and InformationSystems, Kharagpur, INDIA December 8 -10, 2008 |
| Abstract: | Short term load forecasting is very essential to
the operation of electricity companies. It enhances the
energy-efficient and reliable operation of power system.
Artificial Neural Networks are employed for non-linear
short term load forecasting owing to their powerful nonlinear
mapping capabilities. These are generally trained
through back-propagation, genetic algorithm (GA), particle
swarm optimization (PSO) and artificial immune system
(AIS). All these algorithms have specific benefits in terms of
accuracy, speed of convergence and historical data
requirement for training. In this paper a hybrid AIS is
proposed, which is a combination of back-propagation with
AIS to get faster convergence, lesser historical data
requirement for training with a little compromise in
accuracy. |
| Description: | Copyright belongs to the proceedings publisher |
| URI: | http://hdl.handle.net/2080/764 |
| Appears in Collections: | Conference Papers
|
Files in This Item:
| File |
Description |
Size | Format |
|---|
| STLF Using Hybrid AIS Final.pdf | | 198Kb | Adobe PDF | View/Open |
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