A Warning System for Energy Consumption and Anomaly Detection｜國立臺灣科技大學博碩士論文系統

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研究生：	特愛諦 Abdi - Suryadinata Telaga
論文名稱：	A Warning System for Energy Consumption and Anomaly Detection A Warning System for Energy Consumption and Anomaly Detection
指導教授：	周瑞生 Jui-Sheng Chou
口試委員:	陳柏翰 Po-Han Chen 蔡志豐 Chih-Fong Tsai 周建成 Chien-Cheng Chou 楊亦東 I-Tung Yang 謝佑明 Yo-Ming Hsieh
學位類別：	博士 Doctor
系所名稱：	工程學院 - 營建工程系 Department of Civil and Construction Engineering
論文出版年：	2016
畢業學年度：	105
語文別：	英文
論文頁數：	198
中文關鍵詞：	smartmeter 、anomalousconsumption 、feedbackvisualization 、warningsystem 、powerconsumptionprediction 、realtimedetection 、dashboard.
外文關鍵詞：	smart meter, anomalous consumption, feedback visualization, warning system, power consumption prediction, real time detection, dashboard.
相關次數：	點閱：215 下載：0
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Effective feedback is required to reduce building power consumption and carbon emission. Providing information that can draw attention from building manager and tenants is the first step to identify power consumption potential reduction. As anomalous consumption could provide such information, this study proposes an a warning system (WS) that
intelligently analyzes anomalous power consumption from large smart meter data sets of a building space and provides real time anomalous consumption visually to different stakeholders. In this study, anomalous consumption is identified through two stages which are consumption prediction and anomaly detection. Daily real time consumption is predicted by developing daily consumption model using hybrid model neural net ARIMA. Then, anomaly consumption is analyzed by calculating the difference between real and predicted consumption through combination of two-sigma rule and time duration. The WS anomalous consumption dashboard was designed so that office occupants with limited technical skills can understand the energy consumption’s behavior. The contribution of the study is two-fold.
Firstly, the study contributes in formalizing methodology to detect anomalous pattern in large data sets and real time environment of building office space energy consumption. Moreover, the prediction part contributes for electricity planning, while the anomaly detection part contributes to understanding tenant’s consumption behavior. Secondly, WS architecture to
present anomalous power consumption of an office space from component (smart meter and sensor) data to office occupants visually in real-time. Actual electricity data from the smart meters in an actual research office space are used to demonstrate the applicability of the proposed WS.

TABLE OF CONTENTS

CHAPTER 1 INTRODUCTION ....................................................1
1 Research Motivation ...................................................1
2 Research Definition....................................................6
3 Research Objectives ...................................................7
4 Research Questions ....................................................7
5 Dissertation Structure ................................................8
CHAPTER 2 LITERATURE REVIEW ............................................. 11
1 Current Smart Meter Technology ...................................... 11
2 Smart Meter for Monitoring Electricity Consumption .................. 13
3 Anomaly Detection for Energy Consumption ............................ 14
4 A Warning System for Anomaly Detection .............................. 18
CHAPTER 3 RESEARCH METHODOLOGY .......................................... 23
1 Overview of Research Methodology .................................... 23
2 Data Collection ..................................................... 24
2.1 Real Time Data Collection ......................................... 24
2.2 Data Pre-Processing ............................................... 25
3 Prediction Methodology for Anomalous Detection ...................... 28
3.1 K-Means Algorithm ................................................. 28
3.2 Artificial Neural Networks ........................................ 30
3.3 Auto-Regressive Integrated Moving Average ......................... 31
3.4 Neural Network Auto Regressive .................................... 31
4 Evaluation Method of Prediction ..................................... 33
4.1 Two-Sigma Rule for Anomalous Detection ............................ 34
5 A Web-Based Warning System .......................................... 34
5.1 Energy Consumption Data Visualization ............................. 36
5.2 Bar Chart Graph for Anomalous Consumption ......................... 37
5.3 Time-series graph for Energy Consumption .......................... 37
5.4 Key Performance Indicator (KPI) ................................... 37
5.5 Warning System Dashboard .......................................... 38
5.6 Human Perception of Dashboard ..................................... 39
6 Software Development Environment .................................... 40
6.1 Programming Language .............................................. 41
6.1.1 JavaScript for Dashboard Screen ................................. 41
6.1.2 Asynchronous JavaScript and XML (AJAX) for Interactive Dashboard .41
6.1.3 PHP Language for Energy Data .................................... 42
6.1.4 JavaScript Object Notation (JSON) for Data Format ............... 43
6.2 Database for Energy Consumption ................................... 43
6.2.1 MySQL ........................................................... 43
6.2.2 Structured Query Language (SQL) ................................. 44
6.2.3 Procedural Language/Structured Query Language (PL/SQL) .......... 44
6.2.4 Database Stored Procedures ...................................... 44
6.2.5 Database Trigger ................................................ 45
CHAPTER 4 A WARNING SYSTEM DEVELOPMENT ............. .................... 46
1 Application Architecture ............................................ 46
1.1 Three-Tier Architecture ........................................... 46
1.2 Presentation Tier.................................................. 49
1.3 Logic Tier ........................................................ 49
1.4 Data Tier ......................................................... 50
2 Dataflow Design ..................................................... 50
3 Database Management ................................................. 52
4 Dashboard Design .................................................... 60
4.1 Responsive Web .................................................... 60
4.2 Razorflow Framework ................................................61
4.3 Energy Consumption Dashboard ...................................... 62
CHAPTER 5 EXPERIMENTAL DESIGN AND CASE STUDY ............................ 68
1 Experimental Design ................................................. 68
2 Case Study: A Warning on Anomaly Detection .......................... 73
3 Dashboard Visualization Evaluation .................................. 74
3.1 Problem Scenarios ................................................. 74
3.2 WS Dashboard Use Cases ............................................ 76
3.2.1 Check Current Consumption Status ................................ 78
3.2.2 Check Today Detail Usage ........................................ 79
3.2.3 Check Current Minutely Consumption............................... 79
3.2.4 Check Time and Duration of Anomalous Consumption ................ 80
3.2.5 Choose Detail Anomalous Consumption ............................. 80
3.2.6 Check Total Duration Of Anomalous Consumption ................... 81
3.2.7 Plan Daily Energy Demand ........................................ 81
3.2.8 Compare Energy Consumption ...................................... 81
CHAPTER 6 RESULTS AND ANALYSIS .......................................... 83
1 Data Set ............................................................ 83
2 K-means Analysis Results ............................................ 84
3 Correlation Analysis Results ........................................ 85
4 Training Results .................................................... 86
5 Prediction Results .................................................. 89
6 Anomaly Detection Result Analysis ................................... 92
7 Dashboard Evaluation Results ........................................ 97
8 Discussions ......................................................... 98
9 Limitation ......................................................... 100
CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS .............................. 103
1 Concluding Remarks ................................................. 103
2 Research Contributions ............................................. 105
3 Future Research Direction and Recommendations ...................... 106
APPENDIX A. Hardware and Software Requirement .......................... 119
APPENDIX B. Sample of One Day Smart Meter Data ......................... 120
APPENDIX C. Directory Structures of A Warning System ................... 148
APPENDIX D. List of Files .............................................. 150
APPENDIX E. Use Case Description ....................................... 157
APPENDIX F. Data Flow Diagram .......................................... 167
APPENDIX G. Database Diagram of A Warning System ....................... 173
APPENDIX H. JavaScript Code ............................................ 174
APPENDIX I. PHP Code ................................................... 182
APPENDIX J. Batch files code ........................................... 187
APPENDIX K. Text Files Code ............................................ 192
APPENDIX L. MySQL PL/SQL Code .......................................... 193
APPENDIX M. SQL file code............................................... 196

                                

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