태양광 모듈의 열화 조건을 반영한 발전량 예측 모델 개발: AI 기법 접근을 위하여
Development of a Power Generation Forecast Model that Reflects the Degradation Conditions of Solar Modules: For AI Technique Approach
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초록
This study highlights the significance of solar power as a renewable energy source and addresses the challenge of solar module degradation, which comprises the accuracy of power-generation forecasts. Traditional forecasting methods depend on facility capacity and average solar irradiance data; however, these methods are prone to intermittency and high uncertainty. In contrast, this study analyzed the degradation of solar modules and proposed an AI-based forecasting model that incorporates these degradation conditions. Indoor and outdoor tests were conducted to measure the I–V characteristics and assess module degradation conditions under various environmental stresses, resulting in the development of a comprehensive database. The study revealed that calculating the annual degradation rate significantly improved forecast accuracy, reducing the average error rate to 0.82%, thereby achieving the research objectives. This research is noteworthy as it enhances forecasting accuracy by introducing a model that accounts for the degradation of solar modules.
Abstract
This study highlights the significance of solar power as a renewable energy source and addresses the challenge of solar module degradation, which comprises the accuracy of power-generation forecasts. Traditional forecasting methods depend on facility capacity and average solar irradiance data; however, these methods are prone to intermittency and high uncertainty. In contrast, this study analyzed the degradation of solar modules and proposed an AI-based forecasting model that incorporates these degradation conditions. Indoor and outdoor tests were conducted to measure the I–V characteristics and assess module degradation conditions under various environmental stresses, resulting in the development of a comprehensive database. The study revealed that calculating the annual degradation rate significantly improved forecast accuracy, reducing the average error rate to 0.82%, thereby achieving the research objectives. This research is noteworthy as it enhances forecasting accuracy by introducing a model that accounts for the degradation of solar modules.
참고문헌 (14)
- Results of the Finalized Statistics on New and Renewable Energy Supply in 2022 (Announced in 2023), "Korea Energy Agency, Statistical Analysis Division", ~, pp.National Statistical Approval (Consultation) No. 337001 (2023)
- Renewable Energy White Paper, 11-1410000-001321-11, "Korea Energy Agenc", ~ (2022)
- Nam, W, "An Analytial Model for Degradation Rate of High Density Power PV Module using Latent Heat Analysis of Concurrent Temperature and Humidity Cycles with Light Soaking", Doctor, pp.2021
- Kwon, K. -S, "Review of the Special Act on the Promotion of Distributed Energy (Proposal)", Korean Local Government Law Association, 77(23-1), pp.139~180 (2023)
- Kim, W. -S., "Impact of Photovoltaic Generation Expansion on Korea’s Electricity Power System and the Solutions : Focusing on Power System Stability and Supply-and-demand Stability", The Korean Journal of Economic, 27(1), pp.23~65 (2020)
- Naveed Akhter, M., "Review on Forecasting of Photovoltaic Power Generation Based on Machine Learning and Metaheuristic Techniques", IET Renewable Power Generation, 13(7), pp.1009~1023 (2019)
- Lee, J. -I., "Comparison of Solar Power Prediction Model Based on Statistical and Artificial Intelligence Model and Analysis of Revenue for Forecasting Policy", Journal of IKEEE, 26(3), pp.355~363 (2022)
- Song, J. -J., "Analysis of Prediction Model for Solar Power Generation", Journal of Digital Convergence, 12(3), pp.243~248 (2014)
- Kim, K. -D., "The Development of the Short-Term Predict Model for Solar Power Generation", Journal of the Korean Solar Energy Society, 33(6), pp.62~69 (2013)
- Huang, H., "Cloud Motion Estimation for Short Term Solar Irradiation Prediction", SmartGrid Communications IEEE International Conference, pp.696~701 (2013)
- Na, H., "Study on Generation Volume of Floating Solar Power Using Historical Insolation Data", KSCE Journal of Civil and Environment Engineering Research, 43(2), pp.249~258 (2023)
- Mohammad, A., "Revolutionizing Solar Energy : The Impact of Artificial Inteligence on Photovoltaic Systems", International Journal of Multidisciplinary Scurnces and Arts, 2(1), pp.117~127 (2023)
- Cabtillo-Luna, S., "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation", Energies, 16(10), pp.4097~4121 (2023)
- Photovoltaic Devices-Procedures for Temperature and Irraduance Corrections to Measured I-V Characteristics, "Korea Industrial Standards Council", ~ (2005)