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研究生:張嘉媛
研究生(外文):Chia-Yuan Chang
論文名稱:應用長短期記憶遞歸神經網路建立精準的離岸風能預測
論文名稱(外文):Application of long short-term memory recurrent neural networks for accurate offshore wind energy prediction
指導教授:張建成張建成引用關係郭志禹郭志禹引用關係
指導教授(外文):Chien-Cheng ChangChih-Yu Kuo
口試委員:朱錦洲林真真包淳偉宮春斐
口試日期:2019-07-24
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:應用力學研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:85
中文關鍵詞:離岸風能氣象觀測塔中尺度天氣預報長短期記憶遞歸神經網路福海風場東北季風皮爾森相關係數均方根誤差
DOI:10.6342/NTU201902359
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近年來,台灣提倡能再生能源,其中離岸風能為重點發展項目之一。台灣海峽為良好的風場,因此吸引國內外風能公司來台灣西部海岸進行建設及投資,在政府推行離岸風能的政策之下,2015年三家國內離岸風電示範業者分別於苗栗及彰化外海設置三座離岸觀測塔提供風能監測服務。
風場與能源預測對於風能產業以及公部門的電力調度非常重要,觀測塔的即時監測數據可以提升附近風場預測的精準度。在本研究中,我們證明此系統可以透過結合中尺度天氣預報(WRF, the Weather research and forecast model)的模擬資料、氣象觀測塔的觀測資料,以及機器學習模型來實現。觀測塔的資料來源為永傳能源公司2015年9月於福海風場設立的氣象觀測塔所提供的數據。利用長短期記憶遞歸神經網路(LSTM, Long short-term memory recurrent neural networks)模型以觀測塔數據為目標,修正WRF模擬資料來建立人工智慧(AI, Artificial Intelligence)模型,此模型可以用來提供精準的風能預測。
利用皮爾森相關係數(Pearson correlation coefficient),以及均方根誤差(RMSE, the root-mean-square error)做預測準確度評估,用來評估WRF和機器學習的預測結果。預測結果為三天,評估結果顯示,在東北季風時期(10月到隔年4月)的預測精準度可以大幅的提升,因此,在冬季時期可以提供良好的風能預測。未來,機器學習模型可以進一步擴展,提供離岸風能產業準確的風能預測。
Taiwan has been promoting renewable energy vigorously in recent years, of which offshore wind energy is one of the most important energy resources. The Taiwan Strait is an outstanding wind field, so it attracts wind energy operators both from domestic and abroad to invest, construct and operate wind farms. Under the government''s policy of the offshore wind energy, three domestic offshore wind power corporations were appointed and each of them constructed one offshore meteorological masts. They are separately in the offshore areas of Miaoli and Changhua in 2015, serving weather condition monitoring purposes.
Wind field and energy forecast is extremely essential for wind energy industry and power dispatching in public sectors. The real-time data of the wind mast may be incorporated to improve the forecast accuracy for the wind farms in the vicinity area. In this study, we demonstrate that such system can be achieved by combining numerical weather forecast model data (WRF model, the Weather research and forecast model), the offshore wind mast observation data, and machine learning facilities. The project is incorporation with Taiwan Generations Corporation so that the wind mast data are taken from the wind mast of the company. The mast was built in the Fuhai wind farm in September 2015. The LSTM (Long short-term memory recurrent neural networks) machine learning model is applied to train an Artificial Intelligence (AI) model from the WRF forecast to the observation. The AI model is then used to provide accurate wind forecast.
The forecast accuracy is assessed by using the Pearson correlation coefficient and RMSE (the root-mean-square error). They are applied to evaluate the forecasts of both WRF and machine learning model. The forecasts are three-day forecasts. The assessments show that the forecast accuracy can be greatly improved in the northeast monsoon period (October to April). It is expected that excellent wind energy prediction can be achieved in the winter monsoon periods. The machine learning model in future can be further extended to provide accurate wind energy production forecasts prediction for the offshore wind energy industry.
口試委員審定書 i
誌謝 iii
中文摘要 v
Abstract vii
目錄 ix
圖目錄 xi
表目錄 xiii
第一章 緒論 1
1.1 背景介紹&文獻回顧 1
1.2 研究動機 5
1.3 論文架構 5
1.4 貢獻摘要 6
第二章 材料與研究工具 7
2.1 測風平台介紹 7
2.2 WRF介紹 9
2.3 建立模擬環境 13
第三章 類神經網路理論 15
3.1 機器學習 15
3.1.1 人工智慧發展史 15
3.1.2 機器學習分類 17
3.2 類神經網路 18
3.2.1 人工神經元構造 18
3.2.2 單層感知器 21
3.2.3 多層感知器 22
3.2.4 遞歸神經網路 24
3.2.5 長短期記憶遞歸神經網路 27
3.2.6 類神經網路小結 29
第四章 模型訓練與結果討論 31
4.1 數據前處理 31
4.2 訓練資料與預測 32
4.3 LSTM模型建立 33
4.3.1 資料歸一化 33
4.3.2 模型搭建 35
4.4 介紹誤差分析 40
4.5 實驗結果 42
4.5.1 模型訓練與預測結果 44
4.5.2 結果討論 76
第五章 結論與未來展望 79
5.1 結論 79
5.2 未來展望 81
參考文獻 82
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