非侵入式負載監測系統(Non-Intrusive Load Monitoring system, NILMs)係指不必於各負載點裝設感測器與量測器，只需於電力入口端(Power Service Entrance)裝設一組電壓及電流量測器，並藉由分析於電力入口端所量測的電壓及電流訊號與透過各種電力特徵的萃取及負載辨識的技巧等演算法，即可得知監測系統下各負載的電力使用情形。
本文所提之電力特徵(Power Signature)萃取方式，係透過於電力入口端所量測之負載啟動與關閉的原生暫態電流訊號(Raw Transient Current Signal)進行多階層一維離散小波轉換(1-D Discrete Wavelet Transform)，再計算出各階層之電力強度(Power Intensity)，並與傳統電力特徵─實虛功率及啟動暫態能量於監測系統下對負載辨識能力的強弱作一分析與比較，接著監測系統使用內積運算(Inner Product)與倒傳遞類神經網路(Back-Propagation Artificial Nerual Network)進行負載辨識的任務。最後，並可從各案例的負載辨識結果可知，使用本文所提之電力強度特徵優於傳統電力特徵於非侵入式負載監測系統的辨識能力。

Nonintrusive load monitoring systems (NILMs) can determine the operating intervals and power consumptions of electrical loads in a system from measurements made at a centralized location, such as the power service entrance. In contrast to the conventional monitoring system, NILMs reduces sensor costs by installing sensors only at the power service entrance point. The power signature for each load is then extracted from these measurements.
In this thesis, the multiresolution-analysis technique of Discrete Wavelet Transform (DWT) is employed to extract the Power Intensity (PI) from the raw current signal measured from the power service entrance point. Compared to the traditional power signatures such as real power, reactive power, and ture-on transient energy, the PI exhibits strong uniqueness. In fact, as shown in the thesis, the loads can be easily identified by means of the inner product or neural network.

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