本研究對彰濱多場址設備進行獨家的組合，執行資料對比分析以描繪出風力發電機對風速的響應能力，也深刻論證出以風速作為主特徵去預測發電功率的可行性。實際觀察東北季風對年發電量分布的影響，彰顯出更大的風電資源，特別是在秋冬兩季。本研究利用人工智慧來進行風能的預測分析，應用長短期記憶模型更有效地處理時間序列型的資料。風電預測執行以風速為主的輸入策略，並產出優良的預測表現，但預測準確性仍有很大的進步空間，結果也呈現截止模式的難以預測性。
近岸及離岸場址具有很高的資料相關性，得以利用功率曲線合理地評估離岸的發電潛能，並推斷離岸存在更豐富的風能。運用新型風力發電機實質上證實離岸風電能為台灣新能源政策帶來可觀的效益。風能勢必受到自身間歇特性的影響，如果無法對其加以掌控，大規模的風能電網並聯將面臨不可避免的挑戰。風速預測已開發出極佳的預測表現，因此本研究嘗試利用細化的時間尺度搭配各種優化策略以改善風能斜坡預測。但由於無法有效呈現風速變化的跡象，斜坡事件依舊難以被解決。
風浪很大程度地取決於風的影響，故風速與波浪資料的分布十分貼合。透過相關性分析，證實風與波極高的相似性。並以相關性分布推估兩者之間大致存在一小時之內的短時間延遲。然而，正因為波浪資料收集不易，本研究嘗試應用風速作為新型的特徵去預測波浪。此預測策略也結合小波閾值降噪，降低風速的變化性，使風速資料更加擬合波高分布。結果基本上表明東北季風季節有較好的預測性能，但夏季仍是執行運維作業最好的時期。

This research has an exclusive combination towards multisite dataset in Chanbin to brand out a comparative analysis in describing the ability of the response of wind turbine and even have a profound demonstration of feasible wind power prediction keeping wind speed as a base. The major influence from the northeast monsoon (NEM) on an annual power was practically observed showcasing with a larger power, specifically in autumn and winter. This study uses artificial intelligence to carry out wind energy prediction analysis and applies long short-term memory (LSTM) to process time series data more effectively. The wind power prediction implemented a wind-based input and yielded superior performance, but there is still a chance of great room to enhance the prediction accuracy and the outcome also showed the unpredictability of the cut-off mode.
The nearshore and offshore have an effective high wind speed correlation, so that it makes the usage of power curve feasible to assess the power potential and inferred that, there is more abundant wind energy in offshore. The use of new wind turbine essentially proved that the offshore wind power can bring considerate benefits to Taiwan's new energy policy. Wind energy suffers from its own intermittent nature; therefore, power grid will confront the great challenges on large-scale integration of wind energy. This study has made a diverse attempt in using the advanced time scale with various optimization strategies to have an improved wind ramp prediction, since there is a less scope to have an effective display in the wind speed fluctuation, it is even more hard to have a solution.
Wind waves are highly depended on the wind, so the distribution of wind speed and wave height are remarkably close. Through correlation analysis, the high wind-wave similarity was confirmed. There is a short time lag which is less than one hour estimated by the correlation coefficient distributions. However, just because of the difficulty in wave data collection, this study has tried to apply wind speed as a new input to predict wave height. This prediction strategy also combines wavelet soft threshold denoising (WSTD) to reduce the variability of wind speed and make wind speed data more fit the wave height distribution. The finding basically indicated that, there is even more better prediction performance in the NEM season, but summer is still the perfect time to perform works.

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