智慧電網是一種先進的輸電網路，它協調電力網路中的電力供應並即時為生產者、營運商和消費者提供資訊。由於工業4.0的發展與人口增加導致電力需求與日俱增，因此需要穩定的智慧電網系統來滿足大量的電力需求。本研究專注於預測最近提出的分散式智慧電網控制(DSGC)系統的穩定性，該系統考慮了電力消費者和生產者的反應時間、電力價格彈性係數、生產與消耗的電力等要素。
本研究專注於用人工智慧領域中的機器學習方法預測電網穩定性，本研究以集成式學習中的隨機森林、旋轉森林模型為基礎，提出三種新的模型，分別為稀疏旋轉森林(Sparse Rotation Forest)、增強稀疏旋轉森林(Strength Sparse Rotation Forest)、樹加權稀疏旋轉森林(Tree Weighting Sparse Rotation Forest)。
本研究使用RMSE作為模型效能評估指標，利用k-fold交叉驗證的方式確認模型穩定度，且與原始的旋轉森林、隨機森林相比皆有更高的準確率。最後本研究也將所提出的模型與六種經典的機器學習模型進行比較，分別為決策樹、線性回歸模型、K-近鄰、梯度提升樹、支援向量回歸、多層感知器，實驗結果顯示本研究所提出的模型皆有更高的準確率預測智慧電網穩定性。

A smart grid is an advanced transmission grid that coordinates the power supply in the power grid and provides information to producers, operators, and consumers in real time. Due to the development of Industry 4.0 and the increase in population, power demand is increasing daily so a stable smart grid system is required to meet the large power demand. This study predicts the stability of a recently proposed Decentralized Smart Grid Control (DSGC) system that considers factors such as the reaction time of electricity consumers and producers, the electricity price elasticity coefficient, and the electricity produced and consumed.
This thesis focuses on using machine learning methods in the field of artificial intelligence to predict power grid stability. Based on random forest and rotation forest models in integrated learning, this study proposes three novel machine learning models, called Sparse Rotation Forest Model, Strength Sparse Rotation Forest Model, and Tree Weighting Sparse Rotation Forest Model.
We use RMSE as the model performance evaluation index and perform k-fold cross-validation to confirm the stability of the model and has better performance than the original rotation forest and random forest. Finally, this study also compares the proposed model with six classic machine learning models, called decision tree, linear regression model, K-nearest neighbor, gradient boosting decision tree, support vector regression, and multilayer perceptron. The experimental results show that the proposed models all have better performance in predicting the stability of smart grids.

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