臺灣博碩士論文加值系統

有鑑於現有的電力儲能效率仍未臻完美，且在用電高峰時段仍可能發生供電短缺的問題，大多數的電力公司鼓勵其客戶選用時間電價方案，以分散尖峰時段的用電需求至離峰時段，但此一構想之落實須讓各用戶能即時地掌握其用電資訊；此外，隨著物聯網技術的進步，用戶端的智慧插座成為當今管理家庭用電的一種常見方式，然而現有的智慧插座僅能呈現插座上所有電器之總和而非單一電器之用電量。在本研究中，我們深入地探究了電器辨識之議題，並將非監督式演算法整合到模組化的智慧插座中，藉此可識別出插座上的不同電器；具體而言，我們提出以多精細度之密度分群演算法進行電器辨識，藉由調節不同精細度來試著區分電力特徵近似的不同電器，而根據實驗探討之結果，我們的方法在沒有新電器的先備知識情況下亦能達到有效的辨識成果，本研究亦開發了具使用者操作介面之原型系統，使用者可以觀看單一電器的即時功耗，透過與使用者的互動，我們的系統能藉此學習並訓練得到更好的辨識效果。

Electricity may not be economically stored as other forms of energy such that it would be in short supply during the peak time. In view of this difficulty, most power suppliers encourage their customers to adopt time-of-use rate plans. Consequently, it is essential for a user to be able to perceive the real-time information of power consumption. With the advancement of Internet of Things technologies, smart sockets are becoming a commodity to manage power consumption in a household. However, current smart sockets merely present the total electricity consumption rather than the individual consumption of household appliances. In this work, we thus investigate the problem of appliance recognition and implement an unsupervised algorithm on a modular smart socket so as to identify each appliance on the socket. Specifically, we propose to adopt a density-based clustering approach to perform this appliance recognition task. Furthermore, appliances with similar load signatures (or power features) can be identified by considering different data granularities in our approach. Experimental studies show that our approach is feasible even when there is no prior knowledge of new appliances. We also develop a prototype system with graphical user interfaces to present the real-time power consumption of individual appliance. With the user interaction, our system can learn from the user feedback.

Chapter 1 Introduction 1
1.1 Motivation and Overview 1
1.2 Contributions of This Work 2
Chapter 2 Preliminaries 3
2.1 Smart Sockets on the Market 3
2.2 Load Signatures 5
2.3 Problem Definition of Appliance Recognition 5
2.4 Review of Appliance Recognition Techniques 6
2.4.1 Classification Approaches 7
2.4.2 Event-based Clustering Approaches 8
2.4.3 Density-based Clustering Approaches 9
Chapter 3 Design of the Smart Socket and the Recursive Density-based Clustering Approach 11
3.1 Design of Our Smart Socket 11
3.2 Obtaining Electrical Load Signatures 13
3.3 Proposed Density-based Clustering Approach 14
3.4 Process of Our Appliance Recognition Scheme 16
3.4.1 Load Change Detection 17
3.4.2 Load Disaggregation 18
3.4.3 Labeling of Unknown Appliances 19
3.4.4 Representation of a Cluster 19
3.4.5 Recursion with Different Granularities 19
Chapter 4 Prototyping and Empirical Studies 22
4.1 Collecting Data Samples 22
4.2 Hardware Implementation of Our Smart Socket 24
4.3 Experiment #1: Using Different Parameter Settings in DBSCAN 26
4.4 Experiment #2: Recursive DBSCAN with Multiple Granularities 27
4.5 User Interactions 30
Chapter 5 Conclusions and Future Works 31
Bibliography 32

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