使用基因演算法於非侵入式負載監測系統之特徵萃取｜國立臺灣科技大學博碩士論文系統

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研究生：	錢柏青 Po-Ching Chien
論文名稱：	使用基因演算法於非侵入式負載監測系統之特徵萃取 Feature Extraction of Non-Intrusive Load-Monitoring System Using Genetic Algorithm
指導教授：	陳南鳴 Nan-Ming Chen 章學賢 Hsueh-Hsien Chang
口試委員:	連國龍 Kuo-Lung Lian 章學賢 Hsueh-Hsien Chang 陳南鳴 Nan-Ming Chen
學位類別：	碩士 Master
系所名稱：	電資學院 - 電機工程系 Department of Electrical Engineering
論文出版年：	2011
畢業學年度：	99
語文別：	中文
論文頁數：	83
中文關鍵詞：	侵入式負載監測系統、非侵入式負載監測系統、類神經網路、基因演算法
外文關鍵詞：	Intrusive load monitoring system, Non-intrusive load monitoring system, Neural networks, Genetic algorithm
相關次數：	點閱：428 下載：0
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侵入式負載監測系統是在想監測的所有負載上加裝感測器，而非侵入式負載監測系統只需將感測器安裝在電力入口處，藉著量測電力供給入口處的電壓與電流波形資料並加以分析，就能知道各個負載上線或下線的狀況與用電情形。
本論文結合類神經網路與基因演算法使非侵入式負載監測技術可以更精確的辨識負載，並且能改善在多個負載同時操作下的負載辨識成效與所需計算時間。
　　本文以非侵入式負載監測系統研發為基礎，應用在智慧家庭的領域，完成家庭中各項用電設備的負載辨識及建立用電的需量管理，將可使用戶瞭解到建築物裡負載的使用情形，進而減少或關掉不必要負載消耗，以達到節約能源的效果。

An intrusive load monitoring system needs to install sensors for each load. On the other hand, a non-intrusive load monitoring system only needs to install a sensor at the electric power entrance point. By analyzing the voltage and current waveforms data from the electric power entrance point, the power usage of each load can be obtained and then analyzed.
The thesis combines the neural networks and the genetic algorithm to build a non-intrusive load monitoring system to identify the load. It also improves the efficiency of load identification and computational time under multiple operations.
This thesis applies some techniques based on a non-intrusive load monitoring system in smart home to implement the load identification of electric equipments and to manage electric demand. The goal of this thesis is to conserve energy by increasing consumer awareness on their energy usage.

中文摘要...........................................i
英文摘要..........................................ii
誌謝.............................................iii
目錄..............................................iv
圖索引...........................................vii
表索引............................................ix
第一章 緒論........................................1
1 研究背景與動機.................................1
2 研究目的.......................................1
3 文獻回顧.......................................2
3.1 非侵入式負載監測系統.........................3
3.2 基因演算法...................................4
3.3 類神經網路...................................5
4 研究方法.......................................6
5 本論文之貢獻...................................8
6 論文架構.......................................9
第二章 非侵入式負載監測系統.......................10
1 簡介..........................................10
2 非侵入式負載監測的概念與方法..................10
2.1 穩態分析法..................................11
2.2 暫態分析法..................................14
3 非侵入式負載監測系統之應用....................16
4 本章結論......................................17
第三章 基因演算法.................................18
1 簡介..........................................18
2 基因演算法之操作流程..........................19
2.1 編碼與解碼..................................20
2.2 初始族群之決定..............................20
2.3 定義適應函數................................21
2.4 複製........................................21
2.5 交配........................................23
2.6 突變........................................25
2.7 終止條件....................................26
3 基因演算法優缺點..............................27
第四章 類神經網路之非侵入式負載辨識技術...........28
1 簡介......................................... 28
2 應用基因演算法於負載特徵萃取..................28
3 類神經網路....................................31
3.1 簡介........................................31
3.2 倒傳遞類神經網路............................32
4 本章結論......................................36
 第五章 實作結果與分析............................37
1 簡介..........................................37
2 三負載組合案例................................37
2.1 案例1.......................................38
2.2 案例2.......................................41
2.3 案例3.......................................43
2.4 案例4.......................................46
3 非侵入式負載監測系統之LabVIEW圖控模擬平台.....49
3.1 撰寫LabVIEW程式的重點.......................50
3.2 基因演算法程式說明..........................51
3.3 類神經網路程式說明..........................54
3.4 LabVIEW人機介面程式說明.....................56
4 七負載組合的案例..............................61
5 本章結論......................................66
第六章 結論與未來研究方向.........................68
1 結論..........................................68
2 未來研究方向..................................69
參考文獻..........................................70

                                

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