Name
Abstract | ||
|---|---|---|
Neural networks are currently finding practical applications, ranging from 'soft' regulatory control in consumer products, to the accurate modelling of nonlinear systems. Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. In this paper we analyse the problem of short term load forecasting and propose a novel neural network scheme based on the Extended Normalised Radial Basis Function network. The Bayesian Ying Yang Expectation Maximisation algorithm has been used with novel splitting operations to determine a network size and parameter set. The results, utilising data from Eastern Slovakian Energy Board, are then compared with that of an MLP neural network. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.3233/JCM-2011-0389 | J. Comput. Meth. in Science and Engineering |
Keywords | Field | DocType |
function network,mlp neural network,precise load forecasting,neural network,power load forecasting,load forecasting,novel neural network scheme,network size,nonlinear system,novel splitting operation,extended normalised radial basis,short term load forecasting | Mathematical optimization,Radial basis function network,Nonlinear system,Radial basis function,Electric utility,Computer science,Power system simulation,Operations research,Electric power system,Energy management system,Artificial neural network | Journal |
Volume | Issue | ISSN |
11 | 4 | 1472-7978 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vassilis S. Kodogiannis | 1 | 272 | 35.17 |
| Mahdi Amina | 2 | 32 | 2.75 |
| J. N. Lygouras | 3 | 53 | 5.33 |