Enhancing the Performance of Solar Radiation Decomposition Models Using Deep Learning
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Decomposed solar radiation models are commonly used to separate direct and diffuse irradiance from global irradiance. However, most of these models are designed to process hourly data, which may not be sufficient to capture the rapid changes in solar irradiance that occur within a shorter timescale. To address this issue, we examined the performance of existing decomposition models at different temporal resolutions ranging from 1 min to 1 h. We found that the errors in the decomposition models increased as the temporal resolution decreased. Specifically, as the timescale was reduced from hourly to every minute, the relative root-mean-square error(rRMSE) increased by more than 5%. These findings highlight the need to develop accurate models that can process sub-hourly data. Accordingly, we propose the use of deep learning models to estimate the direct irradiance using sub-hourly data. The proposed models significantly reduced the rRMSE by more than 7% compared to the existing models on a 1-min time scale. The results indicate that deep-learning models can provide accurate estimates of direct irradiance, even at sub-hourly temporal resolutions.
Abstract
Decomposed solar radiation models are commonly used to separate direct and diffuse irradiance from global irradiance. However, most of these models are designed to process hourly data, which may not be sufficient to capture the rapid changes in solar irradiance that occur within a shorter timescale. To address this issue, we examined the performance of existing decomposition models at different temporal resolutions ranging from 1 min to 1 h. We found that the errors in the decomposition models increased as the temporal resolution decreased. Specifically, as the timescale was reduced from hourly to every minute, the relative root-mean-square error(rRMSE) increased by more than 5%. These findings highlight the need to develop accurate models that can process sub-hourly data. Accordingly, we propose the use of deep learning models to estimate the direct irradiance using sub-hourly data. The proposed models significantly reduced the rRMSE by more than 7% compared to the existing models on a 1-min time scale. The results indicate that deep-learning models can provide accurate estimates of direct irradiance, even at sub-hourly temporal resolutions.
참고문헌 (11)
- Edward W. Law, "Direct normal irradiance forecasting and its application to concentrated solar thermal output forecasting – A review", Solar Energy, 108, pp.287~307 (2014), Elsevier BV[DOI]
- Miguel de Simón-Martín, "Diffuse solar irradiance estimation on building's façades: Review, classification and benchmarking of 30 models under all sky conditions", Renewable and Sustainable Energy Reviews, 77, pp.783~802 (2017), Elsevier BV[DOI]
- Anne Forstinger, "Expert Quality Control of Solar Radiation Ground Data Sets", Proceedings of the ISES Solar World Congress 2021 (2021), International Solar Energy Society[DOI]
- Dazhi Yang, "Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations", Renewable and Sustainable Energy Reviews, 159, pp.112195~ (2022), Elsevier BV[DOI]
- D.G. Erbs, "Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation", Solar Energy, 28(4), pp.293~302 (1982), Elsevier BV[DOI]
- D.T. Reindl, "Diffuse fraction correlations", Solar Energy, 45(1), pp.1~7 (1990), Elsevier BV[DOI]
- A Quasi-physical Model for Converting Hourly Global Horizontal to Direct Normal Insolation, "Maxwell, E. L.", Sol. Energy Res. Institute, ~ (1987)
- Perez, R. R., "Dynamic Global-to-direct Irradiance Conversion Models", ASHRAE Trans, 98, pp.354~369 (1992)
- Hochreiter, S., "Long Short-Term Memory", Neural Comput, 9(8), pp.1735~1780 (1997)[DOI]
- Cho, K., "Learning Phrase Representations using RNN Encoder-decoder for Statistical Machine Translation", Proceedings of the 2014 Conference on Empirical Methods in Natural Language Process (EMNLP), pp.1724~1734 (2014)
- Rial A. Rajagukguk, "Sun-blocking index from sky image to estimate solar irradiance", Building and Environment, 223, pp.109481~ (2022), Elsevier BV[DOI]