Daily Maximum Electric Load Forecasting with RBF Optimized by AFSA in K-Means Clustering Algorithm

Electric load forecasting is an important aspect in the operation of energy market. Many researchers have tried various methods and have achieved considerable results. In this paper, we used Radial Basis Function Neural Network (RBFN) to train data and forecast daily maximum electric load of a costa...

Full description

Saved in:
Bibliographic Details
Published in:Key engineering materials 2011-01, Vol.467-469 (SUPPL.2), p.1225-1230
Main Authors: Shen, Wei, Sun, Yue Shi
Format: Article
Language:English
Subjects:
Algorithms
Climate
Cluster analysis
Clustering
Fish
Forecasting
Humidity
Neural networks
Trains
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Electric load forecasting is an important aspect in the operation of energy market. Many researchers have tried various methods and have achieved considerable results. In this paper, we used Radial Basis Function Neural Network (RBFN) to train data and forecast daily maximum electric load of a costal city in North China. In order to have a better result, we introduced Artificial Fish Swarm Algorithm (AFSA) to optimize RBF and adjust the center of K-means clustering algorithm. Data mining techniques were also employed to select indicators with impact on electric load. By comparing the forecast values and actual data, we arrived at conclusion that RBF optimized by AFSA could produce accurate result in forecasting daily maximum electric load. We also found that climate factors (temperature, humidity and air-pressure) had significant impact on daily maximum electric load.
ISSN:1013-9826
1662-9795
1662-9795
DOI:10.4028/www.scientific.net/KEM.467-469.1225