非侵入式負載監測系統(Non-intrusive Load Monitoring System, NILM)是用於能源監測與負載辨識的系統，其僅使用一組電壓及電流轉換器於系統的入戶端感測整體電氣訊號波形。如此，與傳統的監測系統作比較，可以大幅地減少感測裝置與安裝的成本。本論文為了用於研究與模擬一般家庭建物配電系統，使用實際建置的單相二線式110V配電系統與單相三線式220V/110V不平衡配電系統作為分析理論架構及研究實際應用的可行性。藉由安裝於Intel Atom嵌入式系統(Embedded System)的非侵入式技術，使用粒子群最佳化演算法(Particle Swarm Optimization, PSO)最佳化倒傳遞人工類神經網路(Back-propagation Artificial Neural Network, BP-ANN)的各項權重值與偏移值。研究的結果顯示，對於單相二線式110V配電系統使用實虛功率電力特徵時網路模擬辨識率75%實際系統辨識率為82.35%；若使用實虛功率及各項電流特徵時，網路模擬辨識率98%實際系統辨識率為100%。對於單相三線式220V/110V不平衡配電系統使用總實虛功率電力特徵(Ptotal, Qtotal)時模擬辨識率97%實際系統辨識率為100%；若使用各相實需功率(Pright, Qright, Pleft, and Qleft) 時，網路模擬與實際系統辨識率則皆為100%。

Non-intrusive load monitoring (NILM) system is an energy demand monitoring and load identification system that only uses voltage and current sensors that are installed at the power service entrance of an electric system. The system is better than traditional intrusive monitoring system because it is able to reduce the cost of sensors and installations. In this study, a real single-phase three-wire unbalanced 220V/110V distribution system model of a residential building is designed and implemented, and some non-intrusive techniques are executed in the Intel Atom Embedded System and a LabView program. To enhance the performance, this thesis proposes Particle Swarm Optimization (PSO) to optimize the parameters of Back-propagation Artificial Neural Network (BP-ANN) for training steady-state power signatures, ex. real and reactive power (P and Q) and current harmonics. The results indicate that the simulation result for using real and reactive power (P and Q) is 75% with actual accuracy of 82.35%, while simulation result for using P, Q, and current harmonics is 98% with actual accuracy of 100% for the single-phase two-wire 110V distribution system; the simulation result for using Ptotal and Qtotal signatures is 97% with 100% actual accuracy; while using Pright, Qright, Pleft, and Qleft has simulation result and actual accuracy of 100% for the single-phase three-wire 220V/110V unbalanced distribution system.

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