非侵入式負載監測(non-intrusive load monitoring, NILM)係透過監測建築物整體用電量，檢測個別電器的能源消耗量。非侵入式負載監測能夠透過分析電壓和電流特性，識別各種電器的使用模式，因此有助於能源節約和管理。為了更有效地實現非侵入式負載分類與識別，本研究提出了一種基於軟投票的集成學習演算法，包括決策樹、K-近鄰演算法和多層感知器(EL-SVDT-KNN-MLP)。在本研究中，使用plug-load appliance identification dataset (PLAID)數據集和worldwide household and industry transient energy dataset (WHITED)數據集中的電壓和電流特徵作為輸入數據。在輸入數據進入EL-SVDT-KNN-MLP之前，該數據集會經過仔細的檢查和預處理。在預處理過程中，將應用六種不同的標準化技術來提高機器學習模型的準確性和可靠性，從而使所提出的演算法更能夠熟練地分類與識別電器。所提出的方法通過在六種不同的標準化技術下比較與其他機器學習演算法的準確率、精確率、召回率和F1 score進行驗證。而結果顯示，所提出的EL-SVDT-KNN-MLP演算法優於本文所探討的其它10種機器學習演算法。

Non-intrusive load monitoring (NILM) detects the energy consumption of individual appliances by monitoring the overall electricity usage in a building. By analyzing voltage and current characteristics, NILM can recognize the usage patterns of various appliances, thus facilitating energy conservation and management. To implement non-intrusive load classification and recognition more effectively, this study proposes an ensemble learning algorithm based on soft voting, which comprises a decision tree, K-nearest neighbor algorithm, and multilayer perceptron (EL-SVDT-KNN-MLP). In this study, the voltage and current features in the plug-load appliance identification dataset (PLAID) and worldwide household and industry transient energy dataset (WHITED) are used as input data. The dataset is examined thoroughly and preprocessed before it is fed into the EL-SVDT-KNN-MLP. During preprocessing, six different normalization techniques are applied to the data to improve the accuracy and reliability of the machine-learning model, thus rendering the proposed algorithm more adept at classifying and recognizing appliances. The proposed method is validated by comparing it with other machine learning algorithms in terms of accuracy, precision, recall, and F1 score under the six different normalization methods. The results show that the proposed EL-SVDT-KNN-MLP algorithm outperforms the other ten machine learning algorithms examined in this thesis.

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