面對全球電力需求持續攀升與邁向淨零排放的趨勢，風力發電作為潔淨能源核心技術備受重視；其中，離岸風電具備高度潛力與長期發展優勢。然而，風場實際營運過程中常面臨風速不穩定、氣象條件變異與風機可利用率低等挑戰，導致發電表現與營運效率難以準確掌握。尤其空氣密度變化對風機輸出功率具有直接影響，但在實務評估中常被簡化為常數處理，易造成風能潛力與發電效率的評估偏差。因此，如何準確掌握氣象因子對風場運行的實質影響，遂成為提升再生能源利用效率的重要課題。
本研究以台灣電力公司示範一期離岸風場為應用案例，透過皮爾森相關係數分析篩選出與發電量密切相關之氣象變數，並根據氣溫與氣壓推算逐時空氣密度，進一步進行統計分析以萃取風場基本氣候特徵。隨後採用風場適用之發電效能、可利用率與運維表現等評估指標，系統性評估風場在不同氣象條件下的運行狀態與發電表現。為深入探討氣象因子對風場營運效能之綜合影響，本研究以風速、風向與空氣密度三項變數進行交叉分類，建立八組氣象情境（A~H），並將各評估指標及發電量計算結果區分為發電量模組、發電效率模組及可利用率模組加以探討。
分析結果顯示，於發電量模組中，夏、秋兩季因空氣密度偏低且風向偏離主軸方向，導致實際發電量相對高估。發電效率模組結果指出，低風速情境下的風場較有效利用風資源進行發電，且影響發電效率的主要氣象因子依序為風速、風向與空氣密度。於可利用率模組中發現，低風速情境下之總停機時數較高，可利用率亦相對偏低，進一步觀察其停機時間分布顯示風速與人為停機時間有進一步改善之空間。

Wind energy is a key clean energy solution to address the growing demand for electricity amid efforts to achieve net-zero carbon emissions. Offshore wind power offers immense potential for large-scale development; however, real-world deployment imposes major challenges. Even after bringing a wind farm online, variable weather conditions, fluctuating wind speeds, and turbine availability issues make it difficult to accurately estimate power output and overall efficiency.
One important but often overlooked factor in performance assessment is air density, which directly affects turbine output. Many practical evaluation methods assume that air density is a constant, but this over-simplification can undermine the accuracy of all corresponding estimates.
This study examined a case study involving the Phase I Demonstration Offshore Wind Farm proposed by the Taiwan Power Company, combining hourly power generation data from 2023 with weather observations from a nearby offshore tower in Changhua. Pearson correlation analysis was used to identify key weather variable most strongly correlated with power output, and statistical methods were used to identify relevant weather patterns. Hourly air density was calculated from recorded temperature and pressure data. System performance was evaluated using three analysis modules pertaining to power output, efficiency, and availability. Wind speed, wind direction, and air density were also grouped together to define eight weather scenarios (A–H).
Our findings revealed that in the summer and fall, reduced air density and off-axis wind directions often led to overestimates of power output. While turbines were better able to exploit available energy under low wind speed conditions, those periods were associated with more downtime and reduced availability. These findings underscore the need to incorporate variability in air density and wind direction in offshore wind farm performance assessments.

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三、網路資料
1.經濟部能源署 （2023），離岸風場角點座標 （TWD97）。網址： https：//www.moeaea.gov.tw/wHandLawsList_File
2.日立公司 （n.d.），風力發電系統。網址：https：//www.hitachi.com.tw/products/business/industrial/wind- turbine/products/htw5000_126/specification/index.html