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研究生:鄧利勝
研究生(外文):VICTOR ANDREAN
論文名稱:A Parallel Bidirectional Long Short-Term Memory Model for Energy Disaggregation
論文名稱(外文):A Parallel Bidirectional Long Short-Term Memory Model for Energy Disaggregation
指導教授:連國龍
指導教授(外文):Kuo-Lung Lian
口試委員:鮑興國李育杰花凱龍蔡孟伸
口試委員(外文):Hsing-Kuo PaoYuh-Jye LeeKai-Lung HuaMen-Shen Tsai
口試日期:2019-01-24
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:58
中文關鍵詞:Non-Intrusive Load MonitoringBidirectional Long-short Term MemoryEnergy Disaggregation
外文關鍵詞:Non-Intrusive Load MonitoringBidirectional Long-short Term MemoryEnergy Disaggregation
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Non-intrusive load monitoring (NILM) is an elegant solution for monitoring energy consumption. NILM was getting popular since the advance of machine learning and deep learning technique. For the past years, there have been some deep learning techniques proposed for NILM. The results have shown that the performance of deep learning models can outperform the prior state of the art of NILM models such as Factorial Hidden Markov Model. A NILM model needs to identify distinctive power patterns of certain appliances in order to monitor the power consumptions. Statistical features (SFs) such as power difference and difference of variant power can be utilized to help the network learn better. As there is no single perfect model that can perfectly fit for everything, based on empirical research, we find that particular SF can be useful at certain type of load. This paper proposes a parallel bidirectional long short-term memory model with SFs to improve learning capability of the network. The proposed method is tested along with some most recent deep learning models on NILM such as DCNN, GLU-Res, BLSTM, and AE. The proposed method can successfully outperform those methods and shows consistent results.
Non-intrusive load monitoring (NILM) is an elegant solution for monitoring energy consumption. NILM was getting popular since the advance of machine learning and deep learning technique. For the past years, there have been some deep learning techniques proposed for NILM. The results have shown that the performance of deep learning models can outperform the prior state of the art of NILM models such as Factorial Hidden Markov Model. A NILM model needs to identify distinctive power patterns of certain appliances in order to monitor the power consumptions. Statistical features (SFs) such as power difference and difference of variant power can be utilized to help the network learn better. As there is no single perfect model that can perfectly fit for everything, based on empirical research, we find that particular SF can be useful at certain type of load. This paper proposes a parallel bidirectional long short-term memory model with SFs to improve learning capability of the network. The proposed method is tested along with some most recent deep learning models on NILM such as DCNN, GLU-Res, BLSTM, and AE. The proposed method can successfully outperform those methods and shows consistent results.
Tables of Contents

Abstract i
Acknowledgements ii
Tables of Contents iii
List of Figures iv
List of Tables v
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Problem Statement 3
1.3 Methodology 4
1.4 Outline 4
CHAPTER 2 RELATED WORKS 5
2.1 Optimization-based Approach 5
2.2 Learning-based Approach 5
CHAPTER 3 NILM SYSTEM AND DATA PROPROCESSING 9
3.1 Data scaling 10
3.2 Window Length Selection 10
3.3 Input to output relation (IOR) 11
CHAPTER 4 STATE OF THE ART OF NILM MODELS 14
4.1 Deep Convolutional Neural Network (DCNN) 14
4.2 GLU-Res 14
4.3 Bidirectional long short-term memory (BLSTM) 16
4.4 Autoencoder (AE) 17
CHAPTER 5 PROPOSED METHOD 19
5.1 Feature extractor 20
5.2 Deep Neural Network (DNN) Model 23
CHAPTER 6 EXPERIMENT AND RESULT 29
CHAPTER 7 CONCLUSION & FUTURE WORK 48
7.1 Conclusion 48
7.2 Future Work 48
REFERENCES 49
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