Long-term load forecasting via a hierarchical neural model with time integrators

A novel hierarchical hybrid neural model to the problem of long-term load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets – one on top of the other –, and a single-layer perceptron. It has application into domains which require time series analysis....

Full description

Saved in:
Bibliographic Details
Published in:Electric power systems research 2007-03, Vol.77 (3), p.371-378
Main Authors: Carpinteiro, Otávio A.S., Leme, Rafael C., de Souza, Antonio C. Zambroni, Pinheiro, Carlos A.M., Moreira, Edmilson M.
Format: Article
Language:English
Subjects:
Applied sciences
Electrical engineering. Electrical power engineering
Electrical power engineering
Exact sciences and technology
Long-term load forecasting
Multilayer perceptron
Neural network
Operation. Load control. Reliability
Power networks and lines
Self-organizing map
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A novel hierarchical hybrid neural model to the problem of long-term load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets – one on top of the other –, and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load and mean-load during the next two years. The results are presented and evaluated in the paper.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2006.03.014