Analysis of distributed power generation forecasting model for power distribution planning

Jintae Cho; Hongjoo Kim; Hosung Ryu; Yongju Son; Sungyun Choi

doi:10.5370/KIEE.2021.70.9.1248

Analysis of distributed power generation forecasting model for power distribution planning

Jintae Cho, Hongjoo Kim, Hosung Ryu, Yongju Son, Sungyun Choi

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	1248-1262
Number of pages	15
Journal	Transactions of the Korean Institute of Electrical Engineers
Volume	70
Issue number	9
DOIs	https://doi.org/10.5370/KIEE.2021.70.9.1248
Publication status	Published - 2021 Sept

Keywords

Deep learning model
Distribution planning
Mid to long-term forecasting
Renewable energy resources

ASJC Scopus subject areas

Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.5370/KIEE.2021.70.9.1248

Cite this

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title = "Analysis of distributed power generation forecasting model for power distribution planning",

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.",

keywords = "Deep learning model, Distribution planning, Mid to long-term forecasting, Renewable energy resources",

author = "Jintae Cho and Hongjoo Kim and Hosung Ryu and Yongju Son and Sungyun Choi",

note = "Funding Information: This work was supported by the KEPCO Research Institute under the project entitled by “A Research of Advanced Distribution Planning System for Mid-Long term (R20DA16)”. Publisher Copyright: Copyright {\textcopyright} The Korean Institute of Electrical Engineers.",

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AU - Choi, Sungyun

N1 - Funding Information: This work was supported by the KEPCO Research Institute under the project entitled by “A Research of Advanced Distribution Planning System for Mid-Long term (R20DA16)”. Publisher Copyright: Copyright © The Korean Institute of Electrical Engineers.

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AB - 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.

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KW - Distribution planning

KW - Mid to long-term forecasting

KW - Renewable energy resources

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Analysis of distributed power generation forecasting model for power distribution planning

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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