Name
Abstract | ||
|---|---|---|
Short - term Load forecast significantly influences the management and pricing of power system. This paper presents a Radial Basis Function network based forecasting system to achieve this ability. A mean square error based training algorithm is applied and analysis is given on the Radial Basis Function selection. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICMLC.2010.5580712 | ICMLC |
Keywords | Field | DocType |
microgrid,neuron,power system management,radial basis function networks,pattern,radial basis function selection,load forecast,power distribution,short-term load forecast,mean square error based training algorithm,radial basis function network,power engineering computing,ann,distributed power generation,rbf network,load forecasting,pricing,mean square error methods,temperature,mean square error,artificial neural networks,power system,radial basis function | Radial basis function network,Radial basis function,Computer science,Distributed power generation,Electric power system,Mean squared error,Load forecasting,Artificial intelligence,Artificial neural network,Microgrid,Machine learning | Conference |
Volume | ISBN | Citations |
6 | 978-1-4244-6526-2 | 2 |
PageRank | References | Authors |
0.57 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang-Yuan Xu | 1 | 9 | 4.57 |
| M. C. Leung | 2 | 2 | 0.57 |
| Long Zhou | 3 | 29 | 11.00 |