由於環境危機和能源枯竭問題，人們著手於開發再生能源及相關技術來替代傳統的化石能源。風能作為最清潔的再生能源之一正受到世界矚目。風能需求在這十年中成倍增長，而且在短期內還會成長得更快。由於許多政府希望實現全球淨零能源目標，風力預測成為更好地管理智能電網中的這種間歇性能源並提高其穩定性和安全性的關鍵問題。
本研究結合多種資料前處理方法，提高原始數據集的完整性，構建用於短期多步風力預測的長短期記憶（LSTM）模型。此外，本研究使用兩個風機變數，轉子轉速和發電機繞組溫度，以更好地捕捉風力發電量的變化，從而獲得更準確的結果。
結果顯示，使用這兩個風機變數作為輸入變量確實有助於提高預測模型的性能。 誤差，MAPE 和 MAE 在不同的時間步預測中都有所下降。此外，本研究還分析了結果的誤差分佈，以確定數據集和模型的問題，以提出研究的未來展望。

Due to the environmental crisis and energy depletion, people are developing renewable energy and related technology to replace traditional fossil-based energy. As one of the cleanest renewable energy, wind power is catching the world’s attention. The wind energy demand is multiplying this decade and will be even faster shortly, especially since many governments want to reach the global net-zero energy goal. Therefore, wind power prediction becomes a critical issue in better managing this intermittent energy in the smart grid and improving its stability and safety.
This research focuses on combining several data pre-processing methods to improve the completeness of the original dataset and building a Long-Short Term Memory (LSTM) model for short-term multi-step wind power prediction. Moreover, This research uses two wind turbine features, Rotor RPM and Generator Winding Temperature, to better catch the change in wind power generation, thus getting a more accurate result.
As a result, the scenario which uses these two turbine features as input variables does help improve the prediction model performance. MAPE and MAE are all improved in different time step predictions. Furthermore, this research also analyses the error distribution of the result to identify the problems of the dataset and model to bring out possible future work.

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