基於不同雲種之多模型短期日射量預測

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：14

、訪客IP：3.142.198.51

姓名

黃信豪(Hsin-Hao Huang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於不同雲種之多模型短期日射量預測
(Multi-model short-term solar irradiance prediction based on different cloud types)

相關論文

★ 影片指定對象臉部置換系統	★ 以單一攝影機實現單指虛擬鍵盤之功能
★ 基於視覺的手寫軌跡注音符號組合辨識系統	★ 利用動態貝氏網路在空照影像中進行車輛偵測
★ 以視訊為基礎之手寫簽名認證	★ 使用膚色與陰影機率高斯混合模型之移動膚色區域偵測
★ 影像中賦予信任等級的群眾切割	★ 航空監控影像之區域切割與分類
★ 在群體人數估計應用中使用不同特徵與回歸方法之分析比較	★ 以視覺為基礎之強韌多指尖偵測與人機介面應用
★ 在夜間受雨滴汙染鏡頭所拍攝的影片下之車流量估計	★ 影像特徵點匹配應用於景點影像檢索
★ 自動感興趣區域切割及遠距交通影像中的軌跡分析	★ 基於回歸模型與利用全天空影像特徵和歷史資訊之短期日射量預測
★ Analysis of the Performance of Different Classifiers for Cloud Detection Application	★ 全天空影像之雲追蹤與太陽遮蔽預測

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著科技進步，能源使用的需求越來越大，其中電力能源更是不可或缺。然而隨著環保意識抬頭與永續能源發展，為了未來人類能有更好的生活品質，進而使再生能源的研究領域越來越受到重視，尤其以太陽能源最受矚目。
因為太陽能源受到重視，其太陽日射量不穩定之特性，會造成太陽能發電電廠的消耗成本增加，故本篇論文就是依太陽能發電的電力控管需求，提出短期的太陽日射量預測機制。傳統的衛星影像可以做大範圍以及長期的預測，但是衛星影像大多以數個小時到一天作為預測單位，無法達到以分鐘為單位之精確短期預測，同時也無法針對特定的小範圍精確預測預測。為了在預測上達到更好的時間和空間解析度，全天空相機被引入作為短期預測之取像裝置。
本篇論文提出一套短時間內日射量預測的系統架構與一套修正預測機制。本篇論文研究的日射量預測系統是以回歸模型(Regression Model)作為基礎，先將全天空影像進行雲的分類後，並搭配日射儀蒐集的資訊當作訓練特徵，根據不同的雲類別訓練出不同的回歸模型。預測時可根據當下的雲類別使用對應的模型搭配歷史資訊進行預測。預測出的結果再利用修正機制改進。修正機制是藉由卡爾曼濾波預測(Kalman Filter predictor)和預測日射量修正公式(Ramp Down Correction Function)並依照融合機制，得到最後的短期預測日射量。最後，本篇論文將用兩種不同時間的資料集來實驗，並驗證與分析。實驗顯示將全天空影像進行雲分類再進行預測，在不同的天氣狀況下，天空的雲層狀況變化差距極大，利用這樣的分類條件可以達到更好的預測效果。另外，本論文提出之修正預測機制，預測日射量修正，以及融合回歸模型和卡爾曼濾波預測模型之機制，可使預測更為準確。

摘要(英)

Renewable energy is growing quickly in the modern society. Many countries have devoted themselves to the development of renewable power. And solar energy is one of the most important renewable energy. To overcome its unstable nature and achieve better utilization, forecasting short-term solar irradiance precisely is a crucial issue. This paper proposes a short-term irradiance prediction framework that based on automatic cloud classification. The cloud types are classified according to the features extracted from all-sky images. Multiple regression models are constructed by different cloud types using historical clearness indices or irradiance values as features. Moreover, ramp-down events are detected and the predicted irradiance is corrected on ramp-down events. The amount of correction is determined by the features extracted from the all-sky images. We also design a Kalman-filter based prediction model with time-varying system matrix. Afterwards, we fuse the prediction results of the regressor and the Kalman filter predictor. Finally, we validate the proposed system with two different datasets. Experiments have shown that incorporating cloud type information can capture different characteristics of irradiance variation under different cloud types. Also, the design of time-varying system matrix is able to improve the prediction accuracy.

關鍵字(中)

★ 太陽日射量估測
★ 回歸模型
★ 卡爾曼濾波
★ 雲分類

關鍵字(英)

★ Solar irradiance prediction
★ Regression Model
★ Kalman filter
★ Cloud Classification

論文目次

內容
摘要 I
目錄 III
圖目錄 V
表目錄 VI
第一章緒論 1
1.1 研究動機 1
1.2 相關文獻 2
1.3 系統流程 3
1.4 論文架構 4
第二章 5
2.1 雲的分類 5
2.2預測目標 9
2.2.1水平面太陽總輻射 9
2.2.2天文輻射 10
2.2.3晴空指數 11
2.3特徵向量 12
2.4模型建立機制與預測 13
第三章預測修正 16
3.1修正流程 16
3.2預測日射量修正公式 17
3.3卡爾曼濾波預測 19
3.3.1利用卡爾曼濾波預測太陽輻射量 19
3.3.2動態調整狀態變換矩陣之機制 20
3.4預測量融合機制 21
第四章實驗結果與分析 23
4.1實驗環境與設備 23
4.2 評估方法 25
4.2.1 交叉驗證法 25
4.2.2 錯誤率評估 25
4.3具天空影像之實驗結果 27
4.4 不具天空影像之實驗資料 39
第五章結論與未來研究方向 47
參考文獻 48

參考文獻

[1] GK. Singh. Solar power generation by Photovoltaic technology: a review. Energy 2013; 53: 1-13.
[2] H. Lund. Renewable energy strategies for sustainable development. Energy 2007; 32: 912-919.
[3] B. Urquhart, CW. Chow, D. Nguyen, J. Kleissl, M. Sengupta, J. Blatchford, D. Jeon. Towards intra-hour solar forecasting using two sky imagers at a large solar power plant. American Solar Energy Society 2012; 1-6.
[4] F. Wang, Z. Mi, S. Su, H. Zhao. Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies 2012; 5: 1355-1370.
[5] M. Marquez, C.F.M. Coimbra. Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Solar Energy 2011; 85: 746-756.
[6] N. Sharma, P. Sharma, D. Irwin, P. Shenoy. Predicting solar generation from weather forecasts using machine learning of the IEEE Conference on Smart Grid Communications 2011;528-533
[7] D. Heinemann, E. Lorenz, M. Girodo. Solar irradiance forecasting for the management of solar energy systems. Energy and Semiconductor Research Laboratory, Energy Meteorology Group, Oldenburg University 2006. 1-6.
[8] A. Heinle, A. Macke, A. Srivastav. Automatic cloud classification of whole sky images. Atmospheric Measurement Techniques Discussions 2010; 3: 269–299.
[9] M. Martínez-Chico, F.J. Batlles, J.L. Bosch. Cloud classification in a mediterranean location using radiation data and sky images. Energy 2011; 36: 4055-4062.
[10] H. Huang, S. Yoo, D. Yu, D. Huang, H. Qin. Correlation and local feature based cloud motion estimation. Proceeding of the Twelfth International Workshop on Multimedia Data Mining. ACM, 2012; 1-9.
[11] R. Marquez, C.F. Coimbra. Intra-hour DNI forecasting based on cloud tracking image analysis. Solar Energy 2013; 91: 327-336.
[12] CL. Fu, HY. Cheng. Predicting solar irradiance with all-sky image features via regression. Solar Energy 2013; 97: 537-550.
[13] HY. Cheng, CC. Yu, SJ. Lin. Bi-model short-term solar irradiance prediction using support vector regressors. Solar Energy 2014; 70: 121-127.
[14] SJ. Lin. Short-term Solar Irradiance Forecasting Based on Regression Model using All-Sky Image Features and Historical Data. National Central University. 2014.
[15] Hsu-Yung Cheng, Chih-Chang Yu, Block Based Cloud Classification with Statistical Features and Distribution of Local Texture Features. Atmospheric Measurement Techniques, vol. 8, pp. 1173–1182, Mar. 2015.
[16] JW. Bugler. The determination of hourly insolation on an inclined plane using a diffuse irradiance model based on hourly measured global horizontal insolation. Solar Energy 1997; 19: 477-491.
[17] R.E. Bird. A simple solar spectral model for direct-normal and diffuse horizontal irradiance. Solar Energy 1984; 32: 467-471.
[18] JW. Bugler. The determination of hourly insolation on an inclined plane using a diffuse irradiance model based on hourly measured global horizontal insolation. Solar Energy 1997; 19: 477-491.
[19] R.E. Bird. A simple solar spectral model for direct-normal and diffuse horizontal irradiance. Solar Energy 1984; 32: 467-471.
[20] R.E. Bird, C. Riordan. Simple solar spectral model for direct and diffuse irradiance on horizontal and tilted planes at the earth′s surface for cloudless atmospheres. Journal of Climate Applied Meteorology 1986; 25: 87–97.
[21] S. Liang, A. Strahler, C. Walthall. Retrieval of land surface albedo from satellite observations: a simulation study. IEEE Geoscience and Remote Sensing Symposium Proceedings 1998; 3: 1286 – 1288.
[22] I. Reda, A. Andreas. Solar position algorithm for solar radiation applications. Solar Energy 2004; 76: 577–589.
[23] K.G.T. Hollands, R.G. Huget. A probability density function for the clearness index with applications. Solar Energy 1983; 30: 195-209.
[24] R. Perez, P. Ineichen, R. Seals, A. Zelenka. Making full use of the clearness index for parameterizing hourly insolation conditions. Solar Energy 1990; 45: 111-114.
[25] L.S. Aiken, S.G. West, S.C. Pitts. Handbook of Psychology. Research methods in psychology. Willey N. Y. 2003; 2: 483-507.
[26] C.M. Douglas, C.R. George, 2007. Applied statistics and probability for engineers 4th edition. 435-447.
[27] D. Basak, S. Pal, D.C. Patranabis. Support vector regression. Neural Information Processing –Letters and Reviews 2007; 11: 203-224.
[28] V. Vapnik, S.E. Golowich, A. Smola. Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 1997; 281-287.
[29] Tsai-Cheng Chang, Tracking Clouds and Predicting occlusion of Sun in All-Sky Images, National Central University. 2014.
[30] D.M. Hawkins. The problem of over-fitting. Journal of Chemical Information and Computer Science 2004; 44: 1-12.
[31] R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence 1995; 14: 1137-1145.
[32] CT. Chiang, YS. Lee, X.R. Li, CC. Liao. A RSCMAC based forecasting for solar irradiance from local weather information. Neural Network (IJCNN), The 2012 International Joint Conference on IEEE 2012; 1-7.
[33] S. Dunne, B Ghosh. Weather Adaptive Traffic Prediction Using Neurowavelet Models. IEEE Transactions on Intelligent Transportation Systems 2013; 14: 370 – 379.
[34] N. Sharma, P. Sharma, D. Irwin, and P. Shenoy. Predicting Solar Generation from Weather Forecasts Using Machine Learning. 2011 IEEE SmartGridComm 2011;528-533.
[35] M. Rizwan, M. Jamil, and D. P. Kothari. Generalized Neural Network Approach for Global Solar Energy Estimation in India. IEEE transaction on sustainable energy 2012; 3: 576 – 584.
[36] H. Beltran, E. Pérez, N. Aparicio. Daily Solar Energy Estimation for MinimizingEnergy Storage Requirements in PV Power Plants. IEEE transaction on sustainable energy 2013, 4: 474 – 481.
[37] A.S. Bin Mohd Shah, H. Yokoyama, and N. Kakimoto. High-Precision Forecasting Model of Solar Irradiance Based on Grid Point Value Data Analysis for an Efficient Photovoltaic System. IEEE transaction on sustainable energy 2015, 6: 474 – 481.
[38] E. Geraldi, F. Romano, and E. Ricciardelli. An Advanced Model for the Estimation of theSurface Solar Irradiance Under All AtmosphericConditions Using MSG/SEVIRI Data. IEEE transactions on geoscience and remote sensing 2012,50: 2934 – 2953.
[39] S. Achleitner, A. Kamthe, T. Liu and A. E. Cerpa. SIPS: Solar Irradiance Prediction System. Information Processing in Sensor Networks, IPSN-14 Proceedings of the 13th International Symposium on 2014, 225 – 236.
[40] “MOXA UPort 1150/1150I,”[Online]
Available: http://www.moxa.com.tw/Product/UPort_1150_1150I.htm.

指導教授

鄭旭詠(Hsu-Yung Cheng)

審核日期

2015-8-5

推文