A load forecasting hybrid method for an isolated power system

Author

Sideratos, G. ; Vitellas, I. ; Hatziargyriou, N.

Author_Institution

Nat. Tech. Univ. of Athens, Athens, Greece

fYear

2011

fDate

25-28 Sept. 2011

Firstpage

1

Lastpage

5

Abstract

This paper presents a load forecasting hybrid model designed for isolated power systems. The proposed model consists of four modules that estimate initially the future load demand and a combination module. Radial basis function neural networks (RBFNNs) are applied to make the initial predictions and multilayer perceptrons (MLPs) are used to combine them. Emphasis is given to the RBFNNs generalization ability developing a self-learning procedure with the Particle Swarm Optimization (PSO) algorithm. Satisfactory results are obtained after the evaluation in the Crete case study.

Keywords

load forecasting; multilayer perceptrons; particle swarm optimisation; power engineering computing; power systems; radial basis function networks; unsupervised learning; MLP; PSO; RBFNN; combination module; future load demand; isolated power system; load forecasting hybrid method; multilayer perceptron; particle swarm optimization; radial basis function neural network; self-learning procedure; Equations; Indexes; Load forecasting; Maintenance engineering; Nonhomogeneous media; Load Forecasting; Multilayer Perceptrons; Particle Swarm Optimization; Radial Basis Function Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent System Application to Power Systems (ISAP), 2011 16th International Conference on

Conference_Location

Hersonissos

Print_ISBN

978-1-4577-0807-7

Electronic_ISBN

978-1-4577-0808-4

Type

conf

DOI

10.1109/ISAP.2011.6082190

Filename

6082190