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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