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2024 Vol.12, Issue 3
A Study on ANN/RNN-based Photovoltaic Generation Forecasting

ANN/RNN 기반 태양광 발전량 예측에 관한 연구

Su Wung Baek1
,
Sung Gi Kwon2
,
Chang Heon Kim3*
,
Gye Choon Park4**
백 수웅1
,
권 성기2
,
김 창헌3*
,
박 계춘4**
1Department of Electrical Engineering, Mokpo National University, Muan-gun, 58554, Korea / Enterprise Support Division, Energy Valley Enterprise Development Institute, Naju-si, 58324, Korea
2Department of Electrical Engineering, Mokpo National University, Muan-gun, 58554, Korea
3Artificial Intelligence & Energy Research Center, Korea Photonics Technology Institute, Gwangju, 61007, Korea
4Department of Electrical Engineering, Mokpo National University, Muan-gun, 58554, Korea
1전기공학과, 목포대학교, 무안군, 58554 / 기업지원실, 에너지밸리기업개발원, 나주시, 58324
2전기공학과, 목포대학교, 무안군, 58554
3AI에너지연구센터, 한국광기술원, 광주광역시, 61007
4전기공학과, 목포대학교, 무안군, 58554
*Corresponding Author
**Corresponding Author
30 September 2024. pp. 49-54
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Abstract

This study proposed a forecasting model that combines ANNs and RNNs to address the intermittency and fluidity of solar power generation. Four prediction models were trained separately based on sky conditions provided by the Korea Meteorological Administration, and insolation was estimated using the ASHRAE Clear-Sky model. The proposed model showed an error rate of 6.5-7.7% based on NMAE, which meets the requirements of power generation prediction. As a result, this study can improve the accuracy of solar power generation forecasting, which can contribute to the stability of power operation and the profitability of power operators.

Keywords
Photovoltaic generation forecasting
ANN
RNN
Solar radiation
Forecast model
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Information
  • Publisher :Korea Photovoltaic Society
  • Publisher(Ko) :한국태양광발전학회
  • Journal Title :Current Photovoltaic Research
  • Volume : 12
  • No :3
  • Pages :49-54
  • Received Date : 2024-08-02
  • Revised Date : 2024-08-28
  • Accepted Date : 2024-09-02
  • DOI :https://doi.org/10.21218/CPR.2024.12.3.049
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