隨著科技的進步，科技突飛猛進的發展，大數據預測分析已成為各個領域最新興的話題之一。在工業4.0的時代，能源是一個不可避免的問題，在過去幾年中，可再生能源的使用大幅增加，越來越多的人從傳統發電轉向太陽能和風能等可再生能源。智慧電網是電力系統的先進概念，它協調系統網絡中的電力和通信。它為生產者、經營者和消費者適時地提供訊息。對家庭、組織、工業和智慧城市等消費領域高效傳導供電，有著極高的需求。在這方面，需要具有穩定系統的智慧電網來滿足動態電力的需求。由於影響電網穩定性的因素很多，因此預測智慧電網的穩定性仍然具有挑戰性。
在本論文中，我們專注於在量子計算中結合經典深度學習和量子深度學習算法來預測電網品質穩定度，我們提出了LSTM的混合量子經典模型，稱之為量子長短期記憶(Quantum Long Short-Term Memory, QLSTM)模型，該模型能夠學習具有時間依賴性的數據，且主要與經典的長短期記憶和熱門的深度學習的模型(如CNN, RNN, MLP)進行比較，分析證明所提出的模型在準確率、精確率、召回率(敏感度)和F1分數指標方面的優越性。實驗結果顯示，本研究所提出的模型在準確率與傳統深度學習模型具有相近的表現，且在特定指標有更好的表現。

With the advancement of science and technology and the rapid development of science and technology, big data predictive analytics has become one of the most emerging topics in various fields. Energy is an unavoidable issue in the age of Industry 4.0, and the use of renewable energy has increased significantly over the past few years, with more and more people switching from traditional power generation to renewable energy sources such as solar and wind power. A smart grid is an advanced power system concept, which coordinates power and communication in the system network. It provides timely information for producers, operators, and consumers. There is a high demand for efficient conductive power supply in consumer areas such as homes, organizations, industries, and smart cities. In this regard, a smart grid with a stable system is required to meet dynamic power demands. Predicting the stability of a smart grid remains challenging due to the many factors that affect grid stability.
In this thesis, we focus on combining classical deep learning and quantum deep learning algorithms in quantum computing to predict power grid quality stability. We propose a hybrid quantum-classical model of LSTM called Quantum Long Short-Term Memory (QLSTM), this model can learn time-dependent data, and it is mainly compared with classic long-term short-term memory and popular deep learning models, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multilayer Perceptron (MLP). The experiment results prove that the proposed model is in superiority in terms of accuracy, precision, recall (sensitivity), and F1 score metrics. Moreover, the proposed model has similar performance to the traditional deep learning model in terms of accuracy, and has better performance in certain indicators.

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