全天空影像雲量估算與極短期輻射量預測能力之評估研究

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姓名

吳秉謙(Bing-Qian Wu) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

全天空影像雲量估算與極短期輻射量預測能力之評估研究
(The evaluation research on cloud cover estimation and very short-term radiation forecast capability of all-sky images)

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[文章引用]

[完整記錄]

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至系統瀏覽論文 (2025-7-31以後開放)

摘要(中)

依照臺灣2050淨零排放路徑及策略，預計再生能源發電占總發電的比例將高達60~70%，而近年來太陽光電的成長也是所有再生能源中最快速的，在2020年時已占所有再生能源總量的六成，若要掌握短時太陽能的發電變化，必須精準掌握雲量與雲的動向，其中雲有很大的時空變異性，如雲量、雲屬、雲厚、雲高、雲的結構及雲與太陽的相對位置等，皆容易使太陽輻射短時內有較大起伏，影響太陽能案場發電效率、電力調度及電網穩定性。本研究將透過目前嘉義氣象站內所架設使用的全天空照相機，透過影像處理與紅藍閾值方法計算出雲量與輻射量，並進一步預測短期雲量及輻射的變化情形，也使用台灣標準地面輻射觀測網(Baseline Surface Radiation Network，BSRN)嘉義站之高解析輻射觀測資料來比對驗證結果。
研究結果顯示，在雲量演算法中使用光學氣膠厚度(Aerosol Optical Depth，AOD)來判定大氣灰濛程度，並設定不同的閾值計算，在高污染灰濛的影像下能得到比原廠雲量產品更精準的雲量。而本研究建立之輻射量模式與BSRN的輻射觀測數據有高度的一致性，其決定係數R2 有 0.94，且RMSE僅55.8W m-2，且觀測數據與模式結果的差值在100 W m-2以內的比例有達到93.1%，顯示此模式能有效掌握極短時輻射快速變化的情形。研究結果也發現透過不同情境之輻射量計算，能將短暫輻射高值的情境區分出來，後續能進一步針對此情境進行改進。而本研究建立之輻射量短期預測方式納入本模式參數的變化趨勢來進行預測，十分鐘內的預測決定係數R2都達0.81以上，RMSE大部分也都低於100 W m-2，其準確度在10分鐘內可達到80.3%，也說明本模式所建立之預測方式能夠掌握輻射短期的快速變化。上述結果在未來除了可應用在其他有架設全天空照相機之測站外，本模式也使用較低的運算資源即可進行，因此可搭配小型電腦與全天空相機模組至太陽能案場實際觀測與計算，提供太陽能相關業者極短時發電變化趨勢之參考。

摘要(英)

According to Taiwan′s 2050 net-zero emission path and strategy, it is projected that renewable energy power generation will comprise up to 60% of the total energy generation. Solar photovoltaics have experienced the most rapid growth among all renewable energy sources in recent years, accounting for 60% of total renewable energy generation in 2020. In order to effectively monitor short-term changes in solar power generation, it is crucial to accurately understand the dynamics of clouds. Clouds exhibit significant temporal and spatial variability, including factors such as cloud cover, cloud type, cloud thickness, cloud altitude, cloud structure, and the relative position of clouds in relation to the sun. Therefore, a comprehensive understanding of these cloud characteristics is essential for accurately predicting solar power generation trends. The solar energy field can experience significant fluctuations in solar radiation within a short timeframe, leading to implications for power generation efficiency, power dispatch, and grid stability. In this research, the calculation of cloud cover and radiation will be conducted using image processing and the red-blue threshold method. The data will be obtained from the all-sky camera that is currently installed in the Chiayi Weather Station. Additionally, the study aims to predict short-term changes in cloud cover and radiation. To validate the results, high-resolution radiation observation data from the Chiayi Station of Taiwan′s Baseline Surface Radiation Network (BSRN) will be utilized for comparison and verification purposes.
The findings indicate that the utilization of Aerosol Optical Depth (AOD) in the cloud cover algorithm allows for the assessment of atmospheric gray masking. By applying various thresholds, a more precise calculation of cloud cover can be achieved compared to the original cloud cover products in the presence of high pollution gray imagery. The radiation model developed in this study demonstrates a strong agreement with the radiation observation data obtained from BSRN. The coefficient of determination (R2) is calculated to be 0.94, indicating a high level of consistency. Additionally, the root-mean-square error (RMSE) is found to be only 55.8W m-2, further supporting the accuracy of the model. The observed data and the model results exhibit a difference of 93.1%, highlighting the model′s ability to effectively capture rapid changes in radiation within a short time period. The findings also indicate that by computing radiation levels in various scenarios, it is possible to differentiate scenarios with high transient radiation values. The present study incorporates a short-term prediction method for radiation levels, which is based on the changing trends of model parameters. The prediction R2, within a ten-minute timeframe, exceeds 0.81, and the majority of the RMSE are below 100 W m-2. Furthermore, the accuracy of the predictions reaches 80.3% within a 10-minute interval. These findings demonstrate that the established prediction method effectively captures rapid short-term fluctuations in radiation levels. In addition to its potential application in other stations equipped with all-sky cameras in the future, this mode can also be executed with limited computing resources. As a result, it can be utilized with a compact computer and an all-sky camera module to observe and calculate the actual solar energy field. This capability offers a valuable reference for monitoring the fluctuation patterns of solar energy-related companies within a brief timeframe.

關鍵字(中)

★ 全天空影像
★ 全天空輻射量
★ 雲量

關鍵字(英)

★ All-sky images
★ Global Horizontal Irradiance
★ Cloud cover

論文目次

摘要 i
Abstract ii
目錄 iv
圖目錄 vi
表目錄 viii
一、前言 1
1-1 研究動機 1
1-2 研究目的 3
二、文獻回顧 5
2-1 全天空影像雲量演算法相關研究 5
2-2 全天空影像輻射量短時預測相關研究 8
三、研究方法 11
3-1 地面觀測資料 12
3-1-1 背景地面輻射觀測網(BSRN) 12
3-1-2 全球氣膠輻射監測網(AERONET) 15
3-1-3 全天空影像照相機 17
3-2 全天空影像雲量計算方法 19
3-2-1 影像裁切 20
3-2-2 太陽遮蔽 21
3-2-3 紅藍閾值法 22
3-3 全天空影像輻射量計算方法 24
3-3-1 計算太陽周遭亮度與過度曝光區域 25
3-3-2 判斷太陽被遮蔽情形 26
3-3-3 太陽理想晴空下輻射值 27
3-3-4 不同情境輻射量計算 28
3-4 全天空影像輻射量短期預測方法 30
3-5 資料誤差分析計算 32
四、結果與討論 33
4-1全天空影像雲量計算 33
4-1-1 原廠與本研究雲量反演法差異 33
4-1-2 不同型號全天空照相機差異 37
4-2 全天空影像輻射量計算 39
4-2-1 輻射量計算成果比較 39
4-2-2 不同情境下輻射量計算成果 42
4-2-3 輻射量計算模式驗證 47
4-3 全天空影像輻射量短期預測 50
五、總結與未來展望 55
5-1 總結 55
5-2 未來展望 56
參考文獻 57

參考文獻

[1] Barta, A., Suhai, B., & Horváth, G. (2014). Polarization Cloud Detection with Imaging Polarimetry. Polarized light and polarization vision in animal sciences, 585-602.
[2]Baseline Surface Radiation Network(2022)。Baseline Surface Radiation Network: Maps。Retrieved from: https://bsrn.awi.de/nc/stations/maps/
[3]Dev, S., Savoy, F. M., Lee, Y. H., & Winkler, S. (2014, May). WAHRSIS: A low-cost high-resolution whole sky imager with near-infrared capabilities. In Infrared imaging systems: Design, analysis, modeling, and testing XXV (Vol. 9071, pp. 510-519). SPIE.
[4]Dev, S., Savoy, F. M., Lee, Y. H., & Winkler, S. (2019). Estimating solar irradiance using sky imagers. Atmospheric Measurement Techniques, 12(10), 5417-5429.
[5]Donatelli, M., & Campbell, G. S. (1998, June). A simple model to estimate global solar radiation. In Proceedings of the 5th European society of agronomy congress, Nitra, Slovak Republic (Vol. 2, pp. 133-134).
[6]Fa, T., Xie, W., Wang, Y., & Xia, Y. (2019). Development of an all-sky imaging system for cloud cover assessment. Applied Optics, 58(20), 5516-5524.
[7]Habte, A., Wilcox, S., & Stoffel, T. (2015). Evaluation of Radiometers Deployed at the National Renewable Energy Laboratory′s Solar Radiation Research Laboratory (No. NREL/TP-5D00-60896). National Renewable Energy Lab.(NREL), Golden, CO (United States).
[8]Haralick, R. M., & Shapiro, L. G. (1992). Computer and robot vision (Vol. 1, pp. 28-48). Reading: Addison-wesley.
[9]Hargreaves, G. H., & Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Applied engineering in agriculture, 1(2), 96-99.
[10]Holben, B. N., Eck, T. F., Slutsker, I. A., Tanré, D., Buis, J. P., Setzer, A., ... & Smirnov, A. (1998). AERONET—A federated instrument network and data archive for aerosol characterization. Remote sensing of environment, 66(1), 1-16.
[11]Ineichen, P., & Perez, R. (2002). A new airmass independent formulation for the Linke turbidity coefficient. Solar Energy, 73(3), 151-157.
[12]Inman, R. H., Pedro, H. T., & Coimbra, C. F. (2013). Solar forecasting methods for renewable energy integration. Progress in energy and combustion science, 39(6), 535-576.
[13]Jayadevan, V. T., Rodriguez, J. J., & Cronin, A. D. (2015). A new contrast-enhancing feature for cloud detection in ground-based sky images. Journal of Atmospheric and Oceanic Technology, 32(2), 209-219.
[14]Johnson, R. W., & Hering, W. S. (1987, June). Automated cloud cover measurements with a solid-state imaging system. In Proceedings of the Cloud Impacts on DOD Operations and Systems—1987, Workshop (pp. 59-69).
[15]Kasten, F. (1980). A simple parameterization of the phyrheliometric formula for determining the Linke turbidity factor. Meteorol. Rdsch., 33, 124-127.
[16]Kasten, F., & Young, A. T. (1989). Revised optical air mass tables and approximation formula. Applied optics, 28(22), 4735-4738.
[17]Li, Q., Lu, W., Yang, J., & Wang, J. Z. (2011). Thin cloud detection of all-sky images using Markov random fields. IEEE Geoscience and remote sensing letters, 9(3), 417-421.
[18]Liu, C. (2009). Beyond pixels: exploring new representations and applications for motion analysis (Doctoral dissertation, Massachusetts Institute of Technology).
[19]Linke, F. (1992). Transmission-koeffizient und trubungsfaktor. Beitr. Phys. Atomos., 10, 91-103.
[20]Long, C. N., & DeLuisi, J. J. (1998, January). Development of an automated hemispheric sky imager for cloud fraction retrievals. In Proc. 10th Symp. on meteorological observations and instrumentation (pp. 171-174).
[21]Long, C. N., Sabburg, J. M., Calbó, J., & Pagès, D. (2006). Retrieving cloud characteristics from ground-based daytime color all-sky images. Journal of Atmospheric and Oceanic Technology, 23(5), 633-652.
[22]Lothon, M., Barnéoud, P., Gabella, O., Lohou, F., Derrien, S., Rondi, S., ... & Montoux, N. (2019). ELIFAN, an algorithm for the estimation of cloud cover from sky imagers. Atmospheric Measurement Techniques, 12(10), 5519-5534.
[23]Meeus, J. (2016). Astronomical algorithms. Richmond.
[24]Murdock, H. E., Gibb, D., André, T., Sawin, J. L., Brown, A., Ranalder, L., ... & Brumer, L. (2021). Renewables 2021-global status report.
[25]Ohmura, A., Dutton, E. G., Forgan, B., Fröhlich, C., Gilgen, H., Hegner, H., ... & Wild, M. (1998). Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research. Bulletin of the American Meteorological Society, 79(10), 2115-2136.
[26]Pelland, S., Remund, J., Kleissl, J., Oozeki, T., & De Brabandere, K. (2013). Photovoltaic and solar forecasting: state of the art. IEA PVPS Task, 14, 1-36.
[27]Singh -Vazquez, M. A., & Kirschen, D. S. (2006, June). Economic impact assessment of load forecast errors considering the cost of interruptions. In 2006 IEEE power engineering society general meeting (pp. 8-pp). IEEE.
[28]Singh, G. K. (2013). Solar power generation by PV (photovoltaic) technology: A review. Energy, 53, 1-13.
[29]Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., & Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable energy, 105, 569-582.
[30]王聖翔 (2021)，活化在地氣象資訊應用系統發展案，活化在地氣象資訊應用系統發展案期末報告。
[31]行政院綠能科技產業推動中心(2022)。國際綠能發展動態。檢自:https://www.geipc.tw/GovInternational.aspx?Menu=3
[32]國研院科技政策研究與資訊中心(2022)。台灣2025年再生能源發電佔比從20%降至15.2%、裝置量目標不變。檢自:https://reurl.cc/DAVg1R

指導教授

王聖翔(Sheng-Hsiang Wang)

審核日期

2023-8-21

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