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Daily Load Forecasting Using the Self-Organizing Map

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Abstract
In this paper, a daily load forecasting algorithm using the self-organizing map(SOM) method is proposed and examined. SOM is a new, powerful software tool for the visualization of high-dimensional data. It requires less training time compared to other networks such as BP learning network, and moreover, its self organizing feature can amend the distorted data. The proposed algorithm analyzes the load patterns of the past couple of years and estimates future load demand by mapping the target day using SOM. KEPCO's hourly load record obtained between 1993 and 1995 is examined to investigate the efficiency of the proposed method. It is shown that the proposed algorithm provides better forecasting results than conventional exponential smoothing method.
In this paper, a daily load forecasting algorithm using the self-organizing map(SOM) method is proposed and examined. SOM is a new, powerful software tool for the visualization of high-dimensional data. It requires less training time compared to other networks such as BP learning network, and moreover, its self organizing feature can amend the distorted data. The proposed algorithm analyzes the load patterns of the past couple of years and estimates future load demand by mapping the target day using SOM. KEPCO's hourly load record obtained between 1993 and 1995 is examined to investigate the efficiency of the proposed method. It is shown that the proposed algorithm provides better forecasting results than conventional exponential smoothing method.
Author(s)
Yang, Myung-KookHwang, Kab-JuCho, Sung-Woo
Issued Date
1998
Type
Research Laboratory
URI
https://oak.ulsan.ac.kr/handle/2021.oak/4083
http://ulsan.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002025286
Alternative Author(s)
양명국황갑주조성우
Publisher
공학연구논문집
Language
eng
Rights
울산대학교 저작물은 저작권에 의해 보호받습니다.
Citation Volume
29
Citation Number
2
Citation Start Page
87
Citation End Page
99
Appears in Collections:
Research Laboratory > Engineering Research
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