Comparative Study of Short-term Electric Load Forecasting

被引：7

作者：

Koo, Bon-gil ^{[1
]}

Lee, Sang-wook ^{[1
]}

Kim, Wook ^{[1
]}

Park, June ho ^{[1
]}

机构：

[1] Pusan Natl Univ, Dept Elect & Comp Engn, Busan, South Korea

来源：

PROCEEDINGS FIFTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS, MODELLING AND SIMULATION | 2014年

基金：

新加坡国家研究基金会;

关键词：

component; formatting; Short-term electric load foreacasting; ARIMA; k-NN; Artificial Neural Network; Simple Exponential Smoothing; GMDH; NEURAL NETWORKS;

D O I：

10.1109/ISMS.2014.85

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we performed short-term electric load forecasting using three methods and compared each results. We classified before making a forecasting model using K-means and k-NN to eliminate error from calendar based classification. Classified load data used as inputs of forecasting model. We compared three methods such as ANN, SES, GMDH. We carried out 1-day ahead prediction for two weeks, January 10 to 16, March 14 to 20, 2011 using hourly Korean electric load data. The results of forecasting, all methods were mostly good in general without applying meteorological data. Most of them, GMDH expressed the most performance in MAPE except for Saturday.

引用

页码：463 / 467

页数：5

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