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Analysis of distributed power generation forecasting model for power distribution planning

Authors
Cho, J.Kim, H.Ryu, H.Son, Y.Choi, S.
Issue Date
2021
Publisher
Korean Institute of Electrical Engineers
Keywords
Deep learning model; Distribution planning; Mid to long-term forecasting; Renewable energy resources
Citation
Transactions of the Korean Institute of Electrical Engineers, v.70, no.9, pp.1248 - 1262
Indexed
SCOPUS
KCI
Journal Title
Transactions of the Korean Institute of Electrical Engineers
Volume
70
Number
9
Start Page
1248
End Page
1262
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138417
DOI
10.5370/KIEE.2021.70.9.1248
ISSN
1975-8359
Abstract
This paper researches a model that predicts a growth in distributed power, taking into account the recent increase in renewable energy interconnected in the distribution system. This paper describes the current state of distributed power and discusses the process of selecting input and output variables for the forecasting model. The, this paper defines various models that can be used for distributed power forecasting and analyze strengths and examples. Finally, this paper compares the utilization of input variables and forecasting models that can be used as mid to long-term distributed power forecasting. Copyright © The Korean Institute of Electrical Engineers.
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공과대학 (전기전자공학부)
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