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研究生:張仁恭
研究生(外文):Chang,Jen-Kung
論文名稱:應用神經網路於風力發電量預測之研究
論文名稱(外文):Wind Power Forecasting Using Neural Network
指導教授:張文宇
口試委員:鍾金明陳正銘張文宇
口試日期:2011-06-23
學位類別:碩士
校院名稱:聖約翰科技大學
系所名稱:電機工程系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:102
中文關鍵詞:風力發電系統、風力發電量預測、類神經網路、倒傳遞神經網路、徑向基底函數神經網路
外文關鍵詞:Wind energy conversion systems, Wind power generation forecasting, Artificial neural network, Back propagation NN, Radial basis function NN
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風力發電系統發電量隨著風速的變化改變,而準確的風力發電系統發電量預測可以幫助風力發電業者降低投資風險。類神經網路是預測的最佳工具,本論文將應用類神經網路來進行風力發電量之預測。本文將分別使用倒傳遞類神經網路和徑向基底函數類神經網路,進行風力發電量的預測。為驗證本文所提出預測方法的有效性,本文將以一部風力發電系統的實際發電量為測試資料。測試結果發現實測值與預測值非常接近,並比較倒傳遞神經網路與徑向基底神經網路之間的誤差,證明本方法兼具準確性與可靠性。
The power generation of wind energy conversion systems (WECS) varies with wind speed. An accurate forecasting method of wind power generation for a WECS can help producer to reduce the risk of loss from investment of WECS. The artificial neural network (ANN) is one of the best tools applied to forecast. The thesis uses the ANN to forecast the wind power generation of WECS. The thesis uses both the back propagation (BP) NN and the radial basis function (RBF) NN to forecast the wind power generation. To demonstrate the effectiveness of the proposed forecasting method, the method is tested on the practical information of wind power generation of a WECS. The good agreements between the realistic values and forecasting values are obtained, the results of the between the BP NN and the RBF NN are error compared. The test results show the proposed method is accurate and reliable.
目錄
致謝 I
中文摘要 II
ABSTRACT III
目錄 IV
圖目錄 VI
表目錄 XIII
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 3
第二章風力發電預測法 4
2.1 風力簡介 4
2.2 風力發電預測方法之文獻回顧 4
2.2.1 時間序列法 4
2.2.2 自適應神經模糊推論系統(Adaptive Neuro-Fuzzy Interface System,ANFIS) 4
2.2.3 類神經網路(Artificial Neural Network, ANN) 5
2.2.4 模糊理論(Fuzzy) 7
2.2.5 統計法 7
2.2.6 貝葉斯法 7
2.2.7 自適應線性 7
2.2.8 模糊神經網路 8
2.2.9 Myciesk法 8
2.2.10 小波分析 8
2.2.11 天氣集合 8
2.2.12 蒙地卡羅模擬法 8
第三章類神經網路簡介 9
3.1 類神經網路介紹 9
3.2 倒傳遞神經網路簡介 13
3.3 徑向基底函數神經網路簡介 15
第四章風力發電量預測系統架構 17
4.1 前言 17
4.2 倒傳遞神經網路預測架構 17
4.3 徑向基底神經網路預測架構 19
4.4 訓練與測試資料 21
第五章實驗結果與分析 22
5.1 實驗方法 22
5.2 1月16日發電量之倒傳遞神經網路測試(IN=3) 22
5.3 1月16日發電量之徑向基底神經網路測試(IN=3) 24
5.4 1月16日發電量之倒傳遞神經網路測試(IN=4) 28
5.5 1月16日發電量之徑向基底神經網路測試(IN=4) 30
5.6 1月16日發電量之倒傳遞神經網路測試(IN=5) 34
5.7 1月16日發電量之徑向基底神經網路測試(IN=5) 36
5.8 1月16日發電量之倒傳遞神經網路測試(IN=6) 40
5.9 1月16日發電量之徑向基底神經網路測試(IN=6) 42
5.10 3月31日發電量之倒傳遞神經網路測試(IN=3) 46
5.11 3月31日發電量之徑向基底神經網路測試(IN=3) 48
5.12 3月31日發電量之倒傳遞神經網路測試(IN=4) 52
5.13 3月31日發電量之徑向基底神經網路測試(IN=4) 54
5.14 3月31日發電量之倒傳遞神經網路測試(IN=5) 58
5.15 3月31日發電量之徑向基底神經網路測試(IN=5) 60
5.16 3月31日發電量之倒傳遞神經網路測試(IN=6) 64
5.17 3月31日發電量之徑向基底神經網路測試(IN=6) 66
5.18 11月19日發電量之倒傳遞神經網路測試(IN=3) 70
5.19 11月19日發電量之徑向基底神經網路測試(IN=3) 72
5.20 11月19日發電量之倒傳遞神經網路測試(IN=4) 76
5.21 11月19日發電量之徑向基底神經網路測試(IN=4) 78
5.22 11月19日發電量之倒傳遞神經網路測試(IN=5) 82
5.23 11月19日發電量之徑向基底神經網路測試(IN=5) 84
5.24 11月19日發電量之倒傳遞神經網路測試(IN=6) 88
5.25 11月19日發電量之徑向基底神經網路測試(IN=6) 90
5.26 實驗結果分析 94
第六章結論與未來發展 96
6.1 結論 96
6.2 未來發展 96
參考文獻 97
作者簡介 102
參考文獻
[1]. http://www.taiwangreenenergy.org.tw/,w96j0. 台灣綠能資訊網。
[2]. M. Negnevitsky, P. Johnson, and S. Santoso, “Short term Wind Power Forecasting Using Hybrid Intelligent Systems,” IEEE Power Engineering Society General Meeting 2007, pp.1-4, 24-28 June 2007.
[3]. C. W. Potter, and M. Negnevitsky, “Very short-term Wind Forecasting for Tasmanian Power Generation,” IEEE Tran. Power System, VOL. 21, NO. 2, pp. 965- 972, May 2006.
[4]. Peter L. Johnson, Michael Negnevitsky, Kashem M. Muttaqi,“Short Term Wind Power Forecasting Using Adaptive Neuro-Fuzzy Inference Systems,” Senior Member,IEEE, pp.1-6.
[5]. M. C. Alexiadis, P. S. Dokopoulos, and H. S. Sahsamanoglou, “Wind Speed and Power Forecasting Based on Spatial Correlation Models,” IEEE Trans Energy Convers, VOL.14, NO. 3, pp. 836-842, Sept. 1999.
[6]. G. N. Kariniotakis, G. S. Stavrakakis, and E. F. Nogaret, “Wind Power Forecasting Using Advanced Neural Networks Models,” IEEE Trans Energy Convers, VOL. 11, NO. 4, pp.762-767, Dec. 1996.
[7]. E. Cadenas, and W. Rivera, “Short term Wind Speed Forecasting in La Venta, Oaxaca, Mexico, Using Artificial Neural Networks,” Renewable Energy, VOL. 34, NO. 1, pp.274-278, Jan. 2009.
[8]. H. Mori, and E. Kurata, “Application of Gaussian Process to Wind Speed Forecasting for Wind Power Generation,” IEEE International Conf. Sustainable Energy Technologies (ICSET), pp. 956-959, 24-27 Nov. 2008.
[9]. S. Fan, J. R. Liao, R. Yokoyama, L. Chen, and W-J Lee, “Forecasting the Wind Generation Using a Two-stage Network Based on Meteorological Information,” IEEE Trans. Energy Convers., VOL.24, NO.2, pp.474-482, June 2009.
[10].T. G. Barbounis, J. B. Theocharis, M. C. Alexiadis, and P. S. Dokopoulos, “Long-term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models,” IEEE Trans. Energy Convers., VOL. 21, NO. 1, pp. 273- 284, March 2006.
[11].T. Senjyu, A. Yona, N. Urasaki, and T. Funabashi, “Application of recurrent neural network to long-term-ahead generating power Forecasting for Wind Power Generator,” IEEE PES Power Systems Conference & Exposition (PSCE) 2006, pp. 1260-1265, Oct.29 -Nov. 1, 2006.
[12].S. S. Sanz, A. P. Bellido, E. O. Garc, A. P. Figueras, L. Prieto, D. Paredes and F.Correoso,“Short-term Wind Speed Prediction by Hybridizing Global and Mesoscale Forecasting Models with Artificial Neural Networks, ” Eighth International Conference on Hybrid Intelligent Systems, IEEE, pp.608-612, 2008.
[13].A. K. Mishra and L. Ramesh, “Application of Neural Networks in Wind Power(Generation) Prediction,” Department of Electrical Engineering at Dr MGR University ,Chennai, INDIA,IEEE, pp.1-5.
[14].M. C. Mabel and E. Fernandez,“Estimation of Energy Yield From Wind Farms Using Artificial Neural Networks,”IEEE Transactions on Energy Conversion, VOL. 24, NO.2, June 2009.
[15].Z. Minghao, J. Dongxiang, L. Chao,“Research on Wind Power Forecasting Method Using Phase Space Reconstruction and Artificial Neural Network,”IEEE, 2007,Beijing , China.
[16].G. Sideratos and N. Hatziargyriou,“Using Radial Basis Neural Networks to Estimate
Wind Power Production,” IEEE,2007.
[17].A. Ghanbarzadeh, A. R. Noghrehabadi, M. A. Behrang, E. Assareh,“Wind Speed Prediction Based on Simple Meteorological Data Using Artificial Neural Network,” 7th IEEE International Conference on Industrial Informatics, 2009.
[18].S. Li, C. Donald, “Using Neural Networks to Estimate Wind Turbine Power Generation,” IEEE Transactions on Energy Conversion, VOL. 16, NO.3, September 2001.
[19].B. Chen, L. Zhao, J.H. Lu, “Wind Power Forecast Using RBF Network and Culture Algorithm. ” IEEE Transform of Scientific and Technological Achievements of Jiangsu Province.
[20].J. Wu, X. Liu, J. Qian, “Wind Speed and Power Forecast Based on RBF Neural Network. ” 2010 International Conference on Computer Application and System Modeling, IEEE.
[21].I. G. Damousis, M. C. Alexiadis, J. B. Theocharis, and P. S. Dokopoulos, “A fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation,” IEEE Trans Energy Convers, VOL. 19, NO. 2, pp. 352- 361, June 2004.
[22].P. Pinson, G. N. Kariniotaki, “Wind Power Forecasting Using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment. ” Paper Accepted for Presentation at 2003 IEEE Bologna Power Tech Conference, June 23-26, Bologna,Italy.
[23].G. Sideratos, and N. D. Hatziargyriou, “An Advanced Statistical Method for Wind Power Forecasting,” IEEE Trans Power System, VOL. 22, NO. 1, pp. 258-265, Feb.2007.
[24].D. P. Mandic, S. Javidi, S.L. Goh, A. Kuh, and K. Aihara, “Complexvalued Prediction of Wind Profile Using Augmented Complex Statistics,” Renewable Energy, VOL. 34, NO. 1, pp. 196-201, Jan. 2009.
[25].M. S. Miranda, and R. W. Dunn, “One-hour-ahead Wind Speed Prediction Using a Bayesian Methodology,” IEEE Power Engineering Society General Meeting 2006, pp.1-6.
[26].A. R. Garcia, and E. De-La-Torre-Vega, “A Statistical Wind Power Forecasting System – A Mexican Wind-farm Case Study,” European Wind Energy Conference & Exhibition – EWEC Parc Chanot, Marseille, France, March 2009.
[27].D. A. Fadare, “The Application of Artificial Neural Networks to Mapping of Wind Speed Profile for Energy Application in Nigeria,” Applied Energy, VOL.87, NO. 3, pp.934-942, Mar. 2010.
[28].P. Pinson and G. N. Kariniotakis, “Wind Power Forecasting Using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment,” Bologna Power Tech Conference, IEEE, June 23-26, Bologna, Italy,2003.
[29].F. O. Hocaoglu, M. Fidan, and O. N. Gerek, “Mycielski Approach for Wind Speed Prediction,” Energy Conversion and Management, VOL . 50, NO. 6, pp. 1436-1443, June 2009.
[30].A. A. Khan, and M. Shahidehpour, “One Day Ahead Wind Speed Forecasting Using Wavelets,” Power Systems Conference and Exposition, 2009, PSCE '09. IEEE Photonic, pp. 1-5, 15-18 March 2009.
[31].J. W. Taylor, P. E. McSharry, and R. Buizza, “Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models,” IEEE Trans. Energy Convers., VOL. 24, NO. 3, pp. 775-782, Sept. 2009.
[32].李家準,「應用蒙地卡羅模擬於太陽能及風力發電量預測」,聖約翰科技大學電機工程研究所碩士論文,2010 年7 月。
[33].張文宇,張仁恭,「應用類神經網路於風力發電量預測之研究」,2010 建構綠能科技與智慧節能產學園區研討會暨成果發表會,2010 年11 月,第176-182 頁。
[34].張斐章,張麗秋,「類神經網路」,初版,東華,台北,2005 年。
[35].吳繼平,「應用類神經網路與基因演算法預測風速與風力發電量」,中原大學電機工程研究所碩士論文, 2007年7月。
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