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系統識別號 U0026-3008201711405200
論文名稱(中文) 智慧電網環境下非侵入式的住宅用電異常偵測
論文名稱(英文) Non-Intrusive Outlier Detection of Individual Residential Energy Consumption in Smart Grid Environment
校院名稱 成功大學
系所名稱(中) 工程科學系
系所名稱(英) Department of Engineering Science
學年度 105
學期 2
出版年 106
研究生(中文) 黃偉鑫
研究生(英文) Wei-Hsin Huang
學號 N96041101
學位類別 碩士
語文別 英文
論文頁數 35頁
口試委員 指導教授-侯廷偉
口試委員-李維聰
口試委員-林志敏
口試委員-鄧維光
中文關鍵字 智慧電網  異常偵測  動態時間扭曲 
英文關鍵字 Smart Grid  Outlier Detection  Dynamic Time Warping 
學科別分類
中文摘要 在智慧電網的環境裡能夠透過資料傳輸及資料分析的技術,提升能源分配及能源使用的效率。近幾年有許多關於應用在智慧電網上的資料分析的研究,其中有關異常用電的偵測一直是重要的議題。本研究提出一種基於使用者的用電歷史數據的非侵入式異常偵測的方式。主要的目的在於透過歷史用電資料,偵測時間區間過高或過低的異常用電數據。研究方法首先挑選被期待相似度高的時間序列數據,再使用動態時間扭曲(Dynamic Time Warping)及K-近鄰演算法(k-nearest neighbors)將被選定的時間序列數據進行分群,挑選出最接近的時間序列數據群。最後偵測的用電數據與前述序列數據群以動態時間扭曲計算差距,進而識別是否有異常的數據。在本研究中以一獨立家庭的四年用電紀錄進行實驗及驗證,結果顯示本研究設計的方法可以達到相較相關研究較高的準確率(79.3%)。
英文摘要 In the smart grid environment, information and communication technology can improve energy distribution and reduce consumption. In recent years, a lot of researches have been proposed and applied in the smart grid environments. The outlier detection of energy consumption is one of the primary challenges. In this research, we proposed a non-intrusive outlier detection method which is based on the individual electricity consumption data. The major objective is to enable an energy management system to be used for detecting anomalies, both of abnormally high and low, energy use. We applied Dynamic Time Warping and k-nearest neighbors to group the patterns of energy consumption. The shortest distance calculated by Dynamic Time Warping to identify abnormal consumption in the same group. A dataset, based on an individual household with the smart meter environment which recorded the electricity usage in almost four years was adopted. The results demonstrated that the proposed method could be used to identify abnormal consumption with 79.3% accuracy. This result is better than related works.
論文目次 摘要 I
Abstract II
誌謝 III
Contents IV
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Organization of Thesis 3
Chapter 2 Related Works 5
2.1 Outlier Detection with Smart Meter 5
2.2 Processing of Time Series Data 6
2.2.1 Dynamic Time Warping 7
2.2.2 Fast Dynamic Time Warping 10
2.3 Clustering Techniques in Data Mining 11
2.3.1 Instance-Based Learning 11
2.3.2 K-Nearest Neighbors 11
Chapter 3 Proposed Method 13
3.1 Requirements 13
3.2 Method Overview 14
3.2.1 Data Extraction Phase 15
3.2.2 DTW-kNN Phase 17
3.2.3 Classification Phase 18
3.3 Effectiveness 18
Chapter 4 Experiments and Evaluation 19
4.1 Experiment Setup 19
4.2 Experimental Results 22
4.3 Evaluation 25
Chapter 5 Conclusions and Future Works 28
5.1 Conclusions 28
5.2 Future Works 29
Reference 31
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