SHORT-TERM LOAD FORECASTING USING DAILY UPDATED LOAD MODELS

被引：6
|
作者：
NAKAMURA, M
机构：
[1] Saga Univ, Dep of Electrical, Engineering, Saga, Jpn, Saga Univ, Dep of Electrical Engineering, Saga, Jpn
来源：
AUTOMATICA | 1985年 / 21卷 / 06期
关键词：
MATHEMATICAL TECHNIQUES - Least Squares Approximations - SIGNAL FILTERING AND PREDICTION - Kalman Filtering;
D O I：
10.1016/0005-1098(85)90046-9
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes one-day-ahead load forecasting using daily updated weekday load models and weekly updated bias models for everyday-of-the-week loads. The load characteristics are examined first for actual data from Kyushu Electric Power Company and weather stations in Kyushu throughout 1982. Then, according to properties of the loads, the algorithm of the load forecasting is derived. Based on actual data, the accuracy of the proposed load forecasting was found to be very high, the standard deviation of the relative error of the load forecast being about 3%.
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页码：729 / 736
页数：8
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