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Electricity peak load forecasting with self‐organizing map and support vector regression
Author(s) -
Fan Shu,
Mao Chengxiong,
Chen Luonan
Publication year - 2006
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.20057
Subject(s) - self organizing map , support vector machine , electricity , computer science , artificial intelligence , electrical load , operator (biology) , set (abstract data type) , data mining , cluster (spacecraft) , machine learning , artificial neural network , engineering , voltage , biochemistry , chemistry , transcription factor , electrical engineering , gene , programming language , repressor
This paper aims to study the short‐term peak load forecasting (PLF) by using Kohonen self‐organizing maps (SOM) and support vector regression (SVR). We first adopt a SOM network to cluster the input data set into several subsets in an unsupervised learning strategy. Then, several SVRs for the next day's peak load are used to fit the training data of each subset in the second stage. In the numerical experiments, data of electricity demand from the New York Independent System Operator (ISO) are used to verify the effectiveness of the prediction for the proposed method. The simulation results show that the proposed model can predict the next day's peak load with a considerably high accuracy compared with the ISO forecasts. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.