近年來隨著經濟發展，人類對於電力能源的需求日漸增加，在全球能量資源有限的情況下，若能即時掌握家中負載使用情形，適時給予用電建議，便可避免能源浪費。為了取得家中用電情形，當前研究多半從總電源取得電力資訊，經由分析辨識後給予使用者回饋，然而當前負載辨識研究大多以單一載具為主，若是針對一般家庭用戶，同時開啟或關閉多台電器是非常普遍的現象。在目前負載辨識相關研究上，多半需要多種電力特徵值輔助與長時間的資料收集，如此一來將造成冗贅資料佔據硬碟空間及資料量傳輸過大等問題。鑑此，本研究提出一負載辨識架構，分析當前智慧電錶所偵測到的電力資訊，動態決定電力特徵值區段傳輸時間，並藉由一因子隱藏馬可夫模型進行負載組合預測，結合動態電力特徵值選取機制進行最終辨識。根據實驗結果得知，多負載的辨識度可達77.49%，若在單一負載的狀況下，辨識度更高達94.59%。整體而言，總系統辨識率可達82.973%。

For the past few years, the needs of electric energy gradually increase to people with economic development. Nowadays, energy resources are severely decreasing, if we can immediately find out the usage of electricity and also provide some recommendations; therefore, it may avoid the waste. In some researches, they obtain information of electricity from general power, and then give feedback to users after analyzing. Most of them usually study for the issue, and they should collect many kinds of power feature for a long time to recognize appliance. However, multi-loading is more common in real condition. In order to reduce redundant and transmissions, we propose a loading recognition framework for analyzing power information and dynamically changing the period of transmissions. First, we use a Factorial Hidden Markov Model to predict the multiple appliance states and select higher rating power features as the input data of appliances recognition algorithm. According to the experiment, the predicted rate of multiple appliance recognition is about 77.49% and 94.59% in single appliance. The overall recognition can reach 82.973%.

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