Non-Intrusive Load Monitoring: Theory, Technologies and Applications
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
Springer Singapore Pte. Limited
2020
|
| Schlagworte: | |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (288 Seiten) |
| ISBN: | 9789811518607 |
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| 505 | 8 | |a Intro -- Preface -- Contents -- Nomenclature -- 1 Introduction -- 1.1 Overview of the Non-intrusive Load Monitoring -- 1.1.1 The Non-intrusive Load Monitoring -- 1.1.2 Overview of Recent Research in Non-intrusive Load Monitoring -- 1.2 Fundamental Key Problems of Non-intrusive Load Monitoring -- 1.2.1 Event Detection in Non-intrusive Load Monitoring -- 1.2.2 Feature Extraction in Non-intrusive Load Monitoring -- 1.2.3 Load Identification in Non-intrusive Load Monitoring -- 1.2.4 Energy Forecasting in Smart Buildings -- 1.3 Scope of This Book -- References -- 2 Detection of Transient Events in Time Series -- 2.1 Introduction -- 2.2 Cumulative Sum Based Transient Event Detection Algorithm -- 2.2.1 Mathematical Description of Change Point Detection -- 2.2.2 Parametric CUSUM Algorithm -- 2.2.3 Non-parametric CUSUM Algorithm -- 2.2.4 Sliding Windows Based on Two-Sided CUSUM Algorithm -- 2.2.5 Original Dataset -- 2.2.6 Evaluation Criteria and Results Analysis -- 2.3 Generalized Likelihood Ratio -- 2.3.1 The Theoretical Basis of GLR -- 2.3.2 Comparison of Event Detection Results -- 2.4 Sequential Probability Ratio Test -- 2.4.1 The Theoretical Basis of SPRT -- 2.4.2 Comparison of Event Detection Results -- 2.5 Experiment Analysis -- 2.5.1 The Results of Three Kinds of Algorithms -- 2.5.2 Conclusion -- References -- 3 Appliance Signature Extraction -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Feature Evaluation Indices -- 3.1.3 Classification Evaluation Indices -- 3.1.4 Data Selection -- 3.2 Features Based on Conventional Physical Definition -- 3.2.1 The Theoretical Basis of Physical Definition Features -- 3.2.2 Feature Extraction -- 3.2.3 Feature Evaluation -- 3.2.4 Classification Results -- 3.3 Features Based on Time-Frequency Analysis -- 3.3.1 The Theoretical Basis of Harmonic Features -- 3.3.2 Feature Extraction -- 3.3.3 Feature Evaluation | |
| 505 | 8 | |a 3.3.4 Classification Results -- 3.4 Features Based on VI Image -- 3.4.1 The Theoretical Basis of VI Image Features -- 3.4.2 Feature Extraction -- 3.4.3 Feature Evaluation -- 3.4.4 Classification Results -- 3.5 Features Based on Adaptive Methods -- 3.5.1 The Theoretical Basis of Adaptive Features -- 3.5.2 Feature Extraction -- 3.5.3 Classification Results -- 3.6 Experimental Analysis -- 3.6.1 Comparative Analysis of Classification Performance -- 3.6.2 Conclusion -- References -- 4 Appliance Identification Based on Template Matching -- 4.1 Introduction -- 4.1.1 Background -- 4.1.2 Data Preprocessing of the PLAID Dataset -- 4.2 Appliance Identification Based on Decision Tree -- 4.2.1 The Theoretical Basis of Decision Tree -- 4.2.2 Steps of Modeling -- 4.2.3 Classification Results -- 4.3 Appliance Identification Based on KNN Algorithm -- 4.3.1 The Theoretical Basis of KNN -- 4.3.2 Steps of Modeling -- 4.3.3 Classification Results -- 4.4 Appliance Identification Based on DTW Algorithm -- 4.4.1 The Theoretical Basis of DTW -- 4.4.2 Steps of Modeling -- 4.4.3 Classification Results -- 4.5 Experiment Analysis -- 4.5.1 Model Framework -- 4.5.2 Comparative Analysis of Classification Performance -- 4.5.3 Conclusion -- References -- 5 Steady-State Current Decomposition Based Appliance Identification -- 5.1 Introduction -- 5.2 Classical Steady-State Current Decomposition Models -- 5.2.1 Model Framework -- 5.2.2 Classical Features of Steady-State Decomposition and the Feature Extraction Method -- 5.2.3 Classical Methods in Steady-State Current Decomposition -- 5.2.4 Performance of the Various Features and Models -- 5.3 Current Decomposition Models Based on Harmonic Phasor -- 5.3.1 Model Framework -- 5.3.2 Novel Features of Steady-State Current Decomposition -- 5.3.3 Multi-objective Optimization Methods in Steady-State Current Decomposition | |
| 505 | 8 | |a 5.3.4 Performance of the Novel Features and Multi-objective Optimization Models -- 5.4 Current Decomposition Models Based on Non-negative Matrix Factor -- 5.4.1 Model Framework -- 5.4.2 Reconstruction of the Data -- 5.4.3 Non-negative Matrix Factorization Method of the Current Decomposition -- 5.4.4 Evaluation of the NMF Method in Current Decomposition -- 5.5 Experiment Analysis -- 5.5.1 Data Generation -- 5.5.2 Comparison Analysis of the Features Used in the Steady-State Decomposition -- 5.5.3 Comparison Analysis of the Models Used in the Steady-State Decomposition -- 5.5.4 Conclusion -- References -- 6 Machine Learning Based Appliance Identification -- 6.1 Introduction -- 6.2 Appliance Identification Based on Extreme Learning Machine -- 6.2.1 The Theoretical Basis of ELM -- 6.2.2 Steps of Modeling -- 6.2.3 Classification Results -- 6.3 Appliance Identification Based on Support Vector Machine -- 6.3.1 The Theoretical Basis of SVM -- 6.3.2 Steps of Modeling -- 6.3.3 Classification Results -- 6.4 Appliance Identification Based on Random Forest -- 6.4.1 The Theoretical Basis of Random Forest -- 6.4.2 Steps of Modeling -- 6.4.3 Classification Results -- 6.5 Experiment Analysis -- 6.5.1 Model Framework -- 6.5.2 Feature Preprocessing for Non-intrusive Load Monitoring -- 6.5.3 Classifier Model Optimization Algorithm for Non-intrusive Load Monitoring -- 6.6 Conclusion -- References -- 7 Hidden Markov Models Based Appliance -- 7.1 Introduction -- 7.2 Appliance Identification Based on Hidden Markov Models -- 7.2.1 Basic Problems Solved by HMM -- 7.2.2 Data Preprocessing -- 7.2.3 Determination of Load Status Information -- 7.3 Appliance Identification Based on Factorial Hidden Markov Models -- 7.3.1 The Theoretical Basis of the FHMM -- 7.3.2 Load Decomposition Steps Based on FHMM -- 7.3.3 Load Power Estimation -- 7.3.4 Decomposition Experiment Based on FHMM. | |
| 505 | 8 | |a 7.3.5 Evaluation Criteria and Result Analysis -- 7.4 Appliance Identification Based on Hidden Semi-Markov Models -- 7.4.1 Hidden Semi-Markov Model -- 7.4.2 Improved Viterbi Algorithm -- 7.4.3 Evaluation Criteria and Result Analysis -- 7.5 Experiment Analysis -- References -- 8 Deep Learning Based Appliance Identification -- 8.1 Introduction -- 8.1.1 Deep Learning -- 8.1.2 NILM Based on Deep Learning -- 8.2 Appliance Identification Based on End-to-End Decomposition -- 8.2.1 Single Feature Based LSTM Network Load Decomposition -- 8.2.2 Multiple Features Based LSTM Network Load Decomposition -- 8.3 Appliance Identification Based on Appliance Classification -- 8.3.1 Appliance Identification Based on CNN -- 8.3.2 Appliance Identification Based on AlexNet -- 8.3.3 Appliance Identification Based on LeNet-SVM Model -- 8.4 Experiment Analysis -- 8.4.1 Experimental Analysis of End-to-End Decomposition -- 8.4.2 Experimental Analysis of Appliance Classification -- References -- 9 Deterministic Prediction of Electric Load Time Series -- 9.1 Introduction -- 9.1.1 Background -- 9.1.2 Advance Prediction Strategies -- 9.1.3 Original Electric Load Time Series -- 9.2 Load Forecasting Based on ARIMA Model -- 9.2.1 Model Framework -- 9.2.2 Theoretical Basis of ARIMA -- 9.2.3 Modeling Steps of ARIMA Predictive Model -- 9.2.4 Predictive Results -- 9.2.5 The Theoretical Basis of EMD -- 9.2.6 Optimization of EMD Decomposition Layers -- 9.2.7 Predictive Results -- 9.3 Load Forecasting Based on Elman Neural Network -- 9.3.1 Model Framework -- 9.3.2 Steps of Modeling -- 9.3.3 Predictive Results -- 9.3.4 Optimization of EMD Decomposition Layers -- 9.3.5 Predictive Results -- 9.4 Experiment Analysis -- 9.4.1 Comparative Analysis of Predictive Performance -- 9.5 Conclusion -- References -- 10 Interval Prediction of Electric Load Time Series -- 10.1 Introduction | |
| 505 | 8 | |a 10.2 Interval Prediction Based on Quantile Regression -- 10.2.1 The Performance Evaluation Metrics -- 10.2.2 Original Sequence for Modeling -- 10.2.3 The Theoretical Basis of Quantile Regression -- 10.2.4 Quantile Regression Based on the Total Electric Load Time Series -- 10.2.5 Quantile Regression Based on Additional Time and Date Information -- 10.2.6 Quantile Regression Based on Information from Sub-meters -- 10.3 Interval Prediction Based on Gaussian Process Filtering -- 10.3.1 The Theoretical Basis of Gaussian Process Regression -- 10.3.2 Gaussian Process Regression Based on the Total Electric Load Time Series -- 10.3.3 Gaussian Process Regression Based on Different Input Features -- 10.3.4 Gaussian Process Regression Based on Feature Selection -- 10.4 Experiment Analysis -- References | |
| 650 | 4 | |a Electric power systems-Load dispatching | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Liu, Hui |t Non-Intrusive Load Monitoring |d Singapore : Springer Singapore Pte. Limited,c2020 |z 9789811518591 |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-033606760 | ||
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| contents | Intro -- Preface -- Contents -- Nomenclature -- 1 Introduction -- 1.1 Overview of the Non-intrusive Load Monitoring -- 1.1.1 The Non-intrusive Load Monitoring -- 1.1.2 Overview of Recent Research in Non-intrusive Load Monitoring -- 1.2 Fundamental Key Problems of Non-intrusive Load Monitoring -- 1.2.1 Event Detection in Non-intrusive Load Monitoring -- 1.2.2 Feature Extraction in Non-intrusive Load Monitoring -- 1.2.3 Load Identification in Non-intrusive Load Monitoring -- 1.2.4 Energy Forecasting in Smart Buildings -- 1.3 Scope of This Book -- References -- 2 Detection of Transient Events in Time Series -- 2.1 Introduction -- 2.2 Cumulative Sum Based Transient Event Detection Algorithm -- 2.2.1 Mathematical Description of Change Point Detection -- 2.2.2 Parametric CUSUM Algorithm -- 2.2.3 Non-parametric CUSUM Algorithm -- 2.2.4 Sliding Windows Based on Two-Sided CUSUM Algorithm -- 2.2.5 Original Dataset -- 2.2.6 Evaluation Criteria and Results Analysis -- 2.3 Generalized Likelihood Ratio -- 2.3.1 The Theoretical Basis of GLR -- 2.3.2 Comparison of Event Detection Results -- 2.4 Sequential Probability Ratio Test -- 2.4.1 The Theoretical Basis of SPRT -- 2.4.2 Comparison of Event Detection Results -- 2.5 Experiment Analysis -- 2.5.1 The Results of Three Kinds of Algorithms -- 2.5.2 Conclusion -- References -- 3 Appliance Signature Extraction -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Feature Evaluation Indices -- 3.1.3 Classification Evaluation Indices -- 3.1.4 Data Selection -- 3.2 Features Based on Conventional Physical Definition -- 3.2.1 The Theoretical Basis of Physical Definition Features -- 3.2.2 Feature Extraction -- 3.2.3 Feature Evaluation -- 3.2.4 Classification Results -- 3.3 Features Based on Time-Frequency Analysis -- 3.3.1 The Theoretical Basis of Harmonic Features -- 3.3.2 Feature Extraction -- 3.3.3 Feature Evaluation 3.3.4 Classification Results -- 3.4 Features Based on VI Image -- 3.4.1 The Theoretical Basis of VI Image Features -- 3.4.2 Feature Extraction -- 3.4.3 Feature Evaluation -- 3.4.4 Classification Results -- 3.5 Features Based on Adaptive Methods -- 3.5.1 The Theoretical Basis of Adaptive Features -- 3.5.2 Feature Extraction -- 3.5.3 Classification Results -- 3.6 Experimental Analysis -- 3.6.1 Comparative Analysis of Classification Performance -- 3.6.2 Conclusion -- References -- 4 Appliance Identification Based on Template Matching -- 4.1 Introduction -- 4.1.1 Background -- 4.1.2 Data Preprocessing of the PLAID Dataset -- 4.2 Appliance Identification Based on Decision Tree -- 4.2.1 The Theoretical Basis of Decision Tree -- 4.2.2 Steps of Modeling -- 4.2.3 Classification Results -- 4.3 Appliance Identification Based on KNN Algorithm -- 4.3.1 The Theoretical Basis of KNN -- 4.3.2 Steps of Modeling -- 4.3.3 Classification Results -- 4.4 Appliance Identification Based on DTW Algorithm -- 4.4.1 The Theoretical Basis of DTW -- 4.4.2 Steps of Modeling -- 4.4.3 Classification Results -- 4.5 Experiment Analysis -- 4.5.1 Model Framework -- 4.5.2 Comparative Analysis of Classification Performance -- 4.5.3 Conclusion -- References -- 5 Steady-State Current Decomposition Based Appliance Identification -- 5.1 Introduction -- 5.2 Classical Steady-State Current Decomposition Models -- 5.2.1 Model Framework -- 5.2.2 Classical Features of Steady-State Decomposition and the Feature Extraction Method -- 5.2.3 Classical Methods in Steady-State Current Decomposition -- 5.2.4 Performance of the Various Features and Models -- 5.3 Current Decomposition Models Based on Harmonic Phasor -- 5.3.1 Model Framework -- 5.3.2 Novel Features of Steady-State Current Decomposition -- 5.3.3 Multi-objective Optimization Methods in Steady-State Current Decomposition 5.3.4 Performance of the Novel Features and Multi-objective Optimization Models -- 5.4 Current Decomposition Models Based on Non-negative Matrix Factor -- 5.4.1 Model Framework -- 5.4.2 Reconstruction of the Data -- 5.4.3 Non-negative Matrix Factorization Method of the Current Decomposition -- 5.4.4 Evaluation of the NMF Method in Current Decomposition -- 5.5 Experiment Analysis -- 5.5.1 Data Generation -- 5.5.2 Comparison Analysis of the Features Used in the Steady-State Decomposition -- 5.5.3 Comparison Analysis of the Models Used in the Steady-State Decomposition -- 5.5.4 Conclusion -- References -- 6 Machine Learning Based Appliance Identification -- 6.1 Introduction -- 6.2 Appliance Identification Based on Extreme Learning Machine -- 6.2.1 The Theoretical Basis of ELM -- 6.2.2 Steps of Modeling -- 6.2.3 Classification Results -- 6.3 Appliance Identification Based on Support Vector Machine -- 6.3.1 The Theoretical Basis of SVM -- 6.3.2 Steps of Modeling -- 6.3.3 Classification Results -- 6.4 Appliance Identification Based on Random Forest -- 6.4.1 The Theoretical Basis of Random Forest -- 6.4.2 Steps of Modeling -- 6.4.3 Classification Results -- 6.5 Experiment Analysis -- 6.5.1 Model Framework -- 6.5.2 Feature Preprocessing for Non-intrusive Load Monitoring -- 6.5.3 Classifier Model Optimization Algorithm for Non-intrusive Load Monitoring -- 6.6 Conclusion -- References -- 7 Hidden Markov Models Based Appliance -- 7.1 Introduction -- 7.2 Appliance Identification Based on Hidden Markov Models -- 7.2.1 Basic Problems Solved by HMM -- 7.2.2 Data Preprocessing -- 7.2.3 Determination of Load Status Information -- 7.3 Appliance Identification Based on Factorial Hidden Markov Models -- 7.3.1 The Theoretical Basis of the FHMM -- 7.3.2 Load Decomposition Steps Based on FHMM -- 7.3.3 Load Power Estimation -- 7.3.4 Decomposition Experiment Based on FHMM. 7.3.5 Evaluation Criteria and Result Analysis -- 7.4 Appliance Identification Based on Hidden Semi-Markov Models -- 7.4.1 Hidden Semi-Markov Model -- 7.4.2 Improved Viterbi Algorithm -- 7.4.3 Evaluation Criteria and Result Analysis -- 7.5 Experiment Analysis -- References -- 8 Deep Learning Based Appliance Identification -- 8.1 Introduction -- 8.1.1 Deep Learning -- 8.1.2 NILM Based on Deep Learning -- 8.2 Appliance Identification Based on End-to-End Decomposition -- 8.2.1 Single Feature Based LSTM Network Load Decomposition -- 8.2.2 Multiple Features Based LSTM Network Load Decomposition -- 8.3 Appliance Identification Based on Appliance Classification -- 8.3.1 Appliance Identification Based on CNN -- 8.3.2 Appliance Identification Based on AlexNet -- 8.3.3 Appliance Identification Based on LeNet-SVM Model -- 8.4 Experiment Analysis -- 8.4.1 Experimental Analysis of End-to-End Decomposition -- 8.4.2 Experimental Analysis of Appliance Classification -- References -- 9 Deterministic Prediction of Electric Load Time Series -- 9.1 Introduction -- 9.1.1 Background -- 9.1.2 Advance Prediction Strategies -- 9.1.3 Original Electric Load Time Series -- 9.2 Load Forecasting Based on ARIMA Model -- 9.2.1 Model Framework -- 9.2.2 Theoretical Basis of ARIMA -- 9.2.3 Modeling Steps of ARIMA Predictive Model -- 9.2.4 Predictive Results -- 9.2.5 The Theoretical Basis of EMD -- 9.2.6 Optimization of EMD Decomposition Layers -- 9.2.7 Predictive Results -- 9.3 Load Forecasting Based on Elman Neural Network -- 9.3.1 Model Framework -- 9.3.2 Steps of Modeling -- 9.3.3 Predictive Results -- 9.3.4 Optimization of EMD Decomposition Layers -- 9.3.5 Predictive Results -- 9.4 Experiment Analysis -- 9.4.1 Comparative Analysis of Predictive Performance -- 9.5 Conclusion -- References -- 10 Interval Prediction of Electric Load Time Series -- 10.1 Introduction 10.2 Interval Prediction Based on Quantile Regression -- 10.2.1 The Performance Evaluation Metrics -- 10.2.2 Original Sequence for Modeling -- 10.2.3 The Theoretical Basis of Quantile Regression -- 10.2.4 Quantile Regression Based on the Total Electric Load Time Series -- 10.2.5 Quantile Regression Based on Additional Time and Date Information -- 10.2.6 Quantile Regression Based on Information from Sub-meters -- 10.3 Interval Prediction Based on Gaussian Process Filtering -- 10.3.1 The Theoretical Basis of Gaussian Process Regression -- 10.3.2 Gaussian Process Regression Based on the Total Electric Load Time Series -- 10.3.3 Gaussian Process Regression Based on Different Input Features -- 10.3.4 Gaussian Process Regression Based on Feature Selection -- 10.4 Experiment Analysis -- References |
| ctrlnum | (ZDB-30-PQE)EBC5997309 (ZDB-30-PAD)EBC5997309 (ZDB-89-EBL)EBL5997309 (OCoLC)1134075889 (DE-599)BVBBV048226030 |
| dewey-full | 621.31700000000001 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.31700000000001 |
| dewey-search | 621.31700000000001 |
| dewey-sort | 3621.31700000000001 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Electronic eBook |
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1.1.2 Overview of Recent Research in Non-intrusive Load Monitoring -- 1.2 Fundamental Key Problems of Non-intrusive Load Monitoring -- 1.2.1 Event Detection in Non-intrusive Load Monitoring -- 1.2.2 Feature Extraction in Non-intrusive Load Monitoring -- 1.2.3 Load Identification in Non-intrusive Load Monitoring -- 1.2.4 Energy Forecasting in Smart Buildings -- 1.3 Scope of This Book -- References -- 2 Detection of Transient Events in Time Series -- 2.1 Introduction -- 2.2 Cumulative Sum Based Transient Event Detection Algorithm -- 2.2.1 Mathematical Description of Change Point Detection -- 2.2.2 Parametric CUSUM Algorithm -- 2.2.3 Non-parametric CUSUM Algorithm -- 2.2.4 Sliding Windows Based on Two-Sided CUSUM Algorithm -- 2.2.5 Original Dataset -- 2.2.6 Evaluation Criteria and Results Analysis -- 2.3 Generalized Likelihood Ratio -- 2.3.1 The Theoretical Basis of GLR -- 2.3.2 Comparison of Event Detection Results -- 2.4 Sequential Probability Ratio Test -- 2.4.1 The Theoretical Basis of SPRT -- 2.4.2 Comparison of Event Detection Results -- 2.5 Experiment Analysis -- 2.5.1 The Results of Three Kinds of Algorithms -- 2.5.2 Conclusion -- References -- 3 Appliance Signature Extraction -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Feature Evaluation Indices -- 3.1.3 Classification Evaluation Indices -- 3.1.4 Data Selection -- 3.2 Features Based on Conventional Physical Definition -- 3.2.1 The Theoretical Basis of Physical Definition Features -- 3.2.2 Feature Extraction -- 3.2.3 Feature Evaluation -- 3.2.4 Classification Results -- 3.3 Features Based on Time-Frequency Analysis -- 3.3.1 The Theoretical Basis of Harmonic Features -- 3.3.2 Feature Extraction -- 3.3.3 Feature Evaluation</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.3.4 Classification Results -- 3.4 Features Based on VI Image -- 3.4.1 The Theoretical Basis of VI Image Features -- 3.4.2 Feature Extraction -- 3.4.3 Feature Evaluation -- 3.4.4 Classification Results -- 3.5 Features Based on Adaptive Methods -- 3.5.1 The Theoretical Basis of Adaptive Features -- 3.5.2 Feature Extraction -- 3.5.3 Classification Results -- 3.6 Experimental Analysis -- 3.6.1 Comparative Analysis of Classification Performance -- 3.6.2 Conclusion -- References -- 4 Appliance Identification Based on Template Matching -- 4.1 Introduction -- 4.1.1 Background -- 4.1.2 Data Preprocessing of the PLAID Dataset -- 4.2 Appliance Identification Based on Decision Tree -- 4.2.1 The Theoretical Basis of Decision Tree -- 4.2.2 Steps of Modeling -- 4.2.3 Classification Results -- 4.3 Appliance Identification Based on KNN Algorithm -- 4.3.1 The Theoretical Basis of KNN -- 4.3.2 Steps of Modeling -- 4.3.3 Classification Results -- 4.4 Appliance Identification Based on DTW Algorithm -- 4.4.1 The Theoretical Basis of DTW -- 4.4.2 Steps of Modeling -- 4.4.3 Classification Results -- 4.5 Experiment Analysis -- 4.5.1 Model Framework -- 4.5.2 Comparative Analysis of Classification Performance -- 4.5.3 Conclusion -- References -- 5 Steady-State Current Decomposition Based Appliance Identification -- 5.1 Introduction -- 5.2 Classical Steady-State Current Decomposition Models -- 5.2.1 Model Framework -- 5.2.2 Classical Features of Steady-State Decomposition and the Feature Extraction Method -- 5.2.3 Classical Methods in Steady-State Current Decomposition -- 5.2.4 Performance of the Various Features and Models -- 5.3 Current Decomposition Models Based on Harmonic Phasor -- 5.3.1 Model Framework -- 5.3.2 Novel Features of Steady-State Current Decomposition -- 5.3.3 Multi-objective Optimization Methods in Steady-State Current Decomposition</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.3.4 Performance of the Novel Features and Multi-objective Optimization Models -- 5.4 Current Decomposition Models Based on Non-negative Matrix Factor -- 5.4.1 Model Framework -- 5.4.2 Reconstruction of the Data -- 5.4.3 Non-negative Matrix Factorization Method of the Current Decomposition -- 5.4.4 Evaluation of the NMF Method in Current Decomposition -- 5.5 Experiment Analysis -- 5.5.1 Data Generation -- 5.5.2 Comparison Analysis of the Features Used in the Steady-State Decomposition -- 5.5.3 Comparison Analysis of the Models Used in the Steady-State Decomposition -- 5.5.4 Conclusion -- References -- 6 Machine Learning Based Appliance Identification -- 6.1 Introduction -- 6.2 Appliance Identification Based on Extreme Learning Machine -- 6.2.1 The Theoretical Basis of ELM -- 6.2.2 Steps of Modeling -- 6.2.3 Classification Results -- 6.3 Appliance Identification Based on Support Vector Machine -- 6.3.1 The Theoretical Basis of SVM -- 6.3.2 Steps of Modeling -- 6.3.3 Classification Results -- 6.4 Appliance Identification Based on Random Forest -- 6.4.1 The Theoretical Basis of Random Forest -- 6.4.2 Steps of Modeling -- 6.4.3 Classification Results -- 6.5 Experiment Analysis -- 6.5.1 Model Framework -- 6.5.2 Feature Preprocessing for Non-intrusive Load Monitoring -- 6.5.3 Classifier Model Optimization Algorithm for Non-intrusive Load Monitoring -- 6.6 Conclusion -- References -- 7 Hidden Markov Models Based Appliance -- 7.1 Introduction -- 7.2 Appliance Identification Based on Hidden Markov Models -- 7.2.1 Basic Problems Solved by HMM -- 7.2.2 Data Preprocessing -- 7.2.3 Determination of Load Status Information -- 7.3 Appliance Identification Based on Factorial Hidden Markov Models -- 7.3.1 The Theoretical Basis of the FHMM -- 7.3.2 Load Decomposition Steps Based on FHMM -- 7.3.3 Load Power Estimation -- 7.3.4 Decomposition Experiment Based on FHMM.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">7.3.5 Evaluation Criteria and Result Analysis -- 7.4 Appliance Identification Based on Hidden Semi-Markov Models -- 7.4.1 Hidden Semi-Markov Model -- 7.4.2 Improved Viterbi Algorithm -- 7.4.3 Evaluation Criteria and Result Analysis -- 7.5 Experiment Analysis -- References -- 8 Deep Learning Based Appliance Identification -- 8.1 Introduction -- 8.1.1 Deep Learning -- 8.1.2 NILM Based on Deep Learning -- 8.2 Appliance Identification Based on End-to-End Decomposition -- 8.2.1 Single Feature Based LSTM Network Load Decomposition -- 8.2.2 Multiple Features Based LSTM Network Load Decomposition -- 8.3 Appliance Identification Based on Appliance Classification -- 8.3.1 Appliance Identification Based on CNN -- 8.3.2 Appliance Identification Based on AlexNet -- 8.3.3 Appliance Identification Based on LeNet-SVM Model -- 8.4 Experiment Analysis -- 8.4.1 Experimental Analysis of End-to-End Decomposition -- 8.4.2 Experimental Analysis of Appliance Classification -- References -- 9 Deterministic Prediction of Electric Load Time Series -- 9.1 Introduction -- 9.1.1 Background -- 9.1.2 Advance Prediction Strategies -- 9.1.3 Original Electric Load Time Series -- 9.2 Load Forecasting Based on ARIMA Model -- 9.2.1 Model Framework -- 9.2.2 Theoretical Basis of ARIMA -- 9.2.3 Modeling Steps of ARIMA Predictive Model -- 9.2.4 Predictive Results -- 9.2.5 The Theoretical Basis of EMD -- 9.2.6 Optimization of EMD Decomposition Layers -- 9.2.7 Predictive Results -- 9.3 Load Forecasting Based on Elman Neural Network -- 9.3.1 Model Framework -- 9.3.2 Steps of Modeling -- 9.3.3 Predictive Results -- 9.3.4 Optimization of EMD Decomposition Layers -- 9.3.5 Predictive Results -- 9.4 Experiment Analysis -- 9.4.1 Comparative Analysis of Predictive Performance -- 9.5 Conclusion -- References -- 10 Interval Prediction of Electric Load Time Series -- 10.1 Introduction</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">10.2 Interval Prediction Based on Quantile Regression -- 10.2.1 The Performance Evaluation Metrics -- 10.2.2 Original Sequence for Modeling -- 10.2.3 The Theoretical Basis of Quantile Regression -- 10.2.4 Quantile Regression Based on the Total Electric Load Time Series -- 10.2.5 Quantile Regression Based on Additional Time and Date Information -- 10.2.6 Quantile Regression Based on Information from Sub-meters -- 10.3 Interval Prediction Based on Gaussian Process Filtering -- 10.3.1 The Theoretical Basis of Gaussian Process Regression -- 10.3.2 Gaussian Process Regression Based on the Total Electric Load Time Series -- 10.3.3 Gaussian Process Regression Based on Different Input Features -- 10.3.4 Gaussian Process Regression Based on Feature Selection -- 10.4 Experiment Analysis -- References</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electric power systems-Load dispatching</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Liu, Hui</subfield><subfield code="t">Non-Intrusive Load Monitoring</subfield><subfield code="d">Singapore : Springer Singapore Pte. Limited,c2020</subfield><subfield code="z">9789811518591</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033606760</subfield></datafield></record></collection> |
| id | DE-604.BV048226030 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:44Z |
| indexdate | 2024-07-10T09:32:30Z |
| institution | BVB |
| isbn | 9789811518607 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033606760 |
| oclc_num | 1134075889 |
| open_access_boolean | |
| physical | 1 Online-Ressource (288 Seiten) |
| psigel | ZDB-30-PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Springer Singapore Pte. Limited |
| record_format | marc |
| spelling | Liu, Hui Verfasser aut Non-Intrusive Load Monitoring Theory, Technologies and Applications Singapore Springer Singapore Pte. Limited 2020 ©2020 1 Online-Ressource (288 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- Preface -- Contents -- Nomenclature -- 1 Introduction -- 1.1 Overview of the Non-intrusive Load Monitoring -- 1.1.1 The Non-intrusive Load Monitoring -- 1.1.2 Overview of Recent Research in Non-intrusive Load Monitoring -- 1.2 Fundamental Key Problems of Non-intrusive Load Monitoring -- 1.2.1 Event Detection in Non-intrusive Load Monitoring -- 1.2.2 Feature Extraction in Non-intrusive Load Monitoring -- 1.2.3 Load Identification in Non-intrusive Load Monitoring -- 1.2.4 Energy Forecasting in Smart Buildings -- 1.3 Scope of This Book -- References -- 2 Detection of Transient Events in Time Series -- 2.1 Introduction -- 2.2 Cumulative Sum Based Transient Event Detection Algorithm -- 2.2.1 Mathematical Description of Change Point Detection -- 2.2.2 Parametric CUSUM Algorithm -- 2.2.3 Non-parametric CUSUM Algorithm -- 2.2.4 Sliding Windows Based on Two-Sided CUSUM Algorithm -- 2.2.5 Original Dataset -- 2.2.6 Evaluation Criteria and Results Analysis -- 2.3 Generalized Likelihood Ratio -- 2.3.1 The Theoretical Basis of GLR -- 2.3.2 Comparison of Event Detection Results -- 2.4 Sequential Probability Ratio Test -- 2.4.1 The Theoretical Basis of SPRT -- 2.4.2 Comparison of Event Detection Results -- 2.5 Experiment Analysis -- 2.5.1 The Results of Three Kinds of Algorithms -- 2.5.2 Conclusion -- References -- 3 Appliance Signature Extraction -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Feature Evaluation Indices -- 3.1.3 Classification Evaluation Indices -- 3.1.4 Data Selection -- 3.2 Features Based on Conventional Physical Definition -- 3.2.1 The Theoretical Basis of Physical Definition Features -- 3.2.2 Feature Extraction -- 3.2.3 Feature Evaluation -- 3.2.4 Classification Results -- 3.3 Features Based on Time-Frequency Analysis -- 3.3.1 The Theoretical Basis of Harmonic Features -- 3.3.2 Feature Extraction -- 3.3.3 Feature Evaluation 3.3.4 Classification Results -- 3.4 Features Based on VI Image -- 3.4.1 The Theoretical Basis of VI Image Features -- 3.4.2 Feature Extraction -- 3.4.3 Feature Evaluation -- 3.4.4 Classification Results -- 3.5 Features Based on Adaptive Methods -- 3.5.1 The Theoretical Basis of Adaptive Features -- 3.5.2 Feature Extraction -- 3.5.3 Classification Results -- 3.6 Experimental Analysis -- 3.6.1 Comparative Analysis of Classification Performance -- 3.6.2 Conclusion -- References -- 4 Appliance Identification Based on Template Matching -- 4.1 Introduction -- 4.1.1 Background -- 4.1.2 Data Preprocessing of the PLAID Dataset -- 4.2 Appliance Identification Based on Decision Tree -- 4.2.1 The Theoretical Basis of Decision Tree -- 4.2.2 Steps of Modeling -- 4.2.3 Classification Results -- 4.3 Appliance Identification Based on KNN Algorithm -- 4.3.1 The Theoretical Basis of KNN -- 4.3.2 Steps of Modeling -- 4.3.3 Classification Results -- 4.4 Appliance Identification Based on DTW Algorithm -- 4.4.1 The Theoretical Basis of DTW -- 4.4.2 Steps of Modeling -- 4.4.3 Classification Results -- 4.5 Experiment Analysis -- 4.5.1 Model Framework -- 4.5.2 Comparative Analysis of Classification Performance -- 4.5.3 Conclusion -- References -- 5 Steady-State Current Decomposition Based Appliance Identification -- 5.1 Introduction -- 5.2 Classical Steady-State Current Decomposition Models -- 5.2.1 Model Framework -- 5.2.2 Classical Features of Steady-State Decomposition and the Feature Extraction Method -- 5.2.3 Classical Methods in Steady-State Current Decomposition -- 5.2.4 Performance of the Various Features and Models -- 5.3 Current Decomposition Models Based on Harmonic Phasor -- 5.3.1 Model Framework -- 5.3.2 Novel Features of Steady-State Current Decomposition -- 5.3.3 Multi-objective Optimization Methods in Steady-State Current Decomposition 5.3.4 Performance of the Novel Features and Multi-objective Optimization Models -- 5.4 Current Decomposition Models Based on Non-negative Matrix Factor -- 5.4.1 Model Framework -- 5.4.2 Reconstruction of the Data -- 5.4.3 Non-negative Matrix Factorization Method of the Current Decomposition -- 5.4.4 Evaluation of the NMF Method in Current Decomposition -- 5.5 Experiment Analysis -- 5.5.1 Data Generation -- 5.5.2 Comparison Analysis of the Features Used in the Steady-State Decomposition -- 5.5.3 Comparison Analysis of the Models Used in the Steady-State Decomposition -- 5.5.4 Conclusion -- References -- 6 Machine Learning Based Appliance Identification -- 6.1 Introduction -- 6.2 Appliance Identification Based on Extreme Learning Machine -- 6.2.1 The Theoretical Basis of ELM -- 6.2.2 Steps of Modeling -- 6.2.3 Classification Results -- 6.3 Appliance Identification Based on Support Vector Machine -- 6.3.1 The Theoretical Basis of SVM -- 6.3.2 Steps of Modeling -- 6.3.3 Classification Results -- 6.4 Appliance Identification Based on Random Forest -- 6.4.1 The Theoretical Basis of Random Forest -- 6.4.2 Steps of Modeling -- 6.4.3 Classification Results -- 6.5 Experiment Analysis -- 6.5.1 Model Framework -- 6.5.2 Feature Preprocessing for Non-intrusive Load Monitoring -- 6.5.3 Classifier Model Optimization Algorithm for Non-intrusive Load Monitoring -- 6.6 Conclusion -- References -- 7 Hidden Markov Models Based Appliance -- 7.1 Introduction -- 7.2 Appliance Identification Based on Hidden Markov Models -- 7.2.1 Basic Problems Solved by HMM -- 7.2.2 Data Preprocessing -- 7.2.3 Determination of Load Status Information -- 7.3 Appliance Identification Based on Factorial Hidden Markov Models -- 7.3.1 The Theoretical Basis of the FHMM -- 7.3.2 Load Decomposition Steps Based on FHMM -- 7.3.3 Load Power Estimation -- 7.3.4 Decomposition Experiment Based on FHMM. 7.3.5 Evaluation Criteria and Result Analysis -- 7.4 Appliance Identification Based on Hidden Semi-Markov Models -- 7.4.1 Hidden Semi-Markov Model -- 7.4.2 Improved Viterbi Algorithm -- 7.4.3 Evaluation Criteria and Result Analysis -- 7.5 Experiment Analysis -- References -- 8 Deep Learning Based Appliance Identification -- 8.1 Introduction -- 8.1.1 Deep Learning -- 8.1.2 NILM Based on Deep Learning -- 8.2 Appliance Identification Based on End-to-End Decomposition -- 8.2.1 Single Feature Based LSTM Network Load Decomposition -- 8.2.2 Multiple Features Based LSTM Network Load Decomposition -- 8.3 Appliance Identification Based on Appliance Classification -- 8.3.1 Appliance Identification Based on CNN -- 8.3.2 Appliance Identification Based on AlexNet -- 8.3.3 Appliance Identification Based on LeNet-SVM Model -- 8.4 Experiment Analysis -- 8.4.1 Experimental Analysis of End-to-End Decomposition -- 8.4.2 Experimental Analysis of Appliance Classification -- References -- 9 Deterministic Prediction of Electric Load Time Series -- 9.1 Introduction -- 9.1.1 Background -- 9.1.2 Advance Prediction Strategies -- 9.1.3 Original Electric Load Time Series -- 9.2 Load Forecasting Based on ARIMA Model -- 9.2.1 Model Framework -- 9.2.2 Theoretical Basis of ARIMA -- 9.2.3 Modeling Steps of ARIMA Predictive Model -- 9.2.4 Predictive Results -- 9.2.5 The Theoretical Basis of EMD -- 9.2.6 Optimization of EMD Decomposition Layers -- 9.2.7 Predictive Results -- 9.3 Load Forecasting Based on Elman Neural Network -- 9.3.1 Model Framework -- 9.3.2 Steps of Modeling -- 9.3.3 Predictive Results -- 9.3.4 Optimization of EMD Decomposition Layers -- 9.3.5 Predictive Results -- 9.4 Experiment Analysis -- 9.4.1 Comparative Analysis of Predictive Performance -- 9.5 Conclusion -- References -- 10 Interval Prediction of Electric Load Time Series -- 10.1 Introduction 10.2 Interval Prediction Based on Quantile Regression -- 10.2.1 The Performance Evaluation Metrics -- 10.2.2 Original Sequence for Modeling -- 10.2.3 The Theoretical Basis of Quantile Regression -- 10.2.4 Quantile Regression Based on the Total Electric Load Time Series -- 10.2.5 Quantile Regression Based on Additional Time and Date Information -- 10.2.6 Quantile Regression Based on Information from Sub-meters -- 10.3 Interval Prediction Based on Gaussian Process Filtering -- 10.3.1 The Theoretical Basis of Gaussian Process Regression -- 10.3.2 Gaussian Process Regression Based on the Total Electric Load Time Series -- 10.3.3 Gaussian Process Regression Based on Different Input Features -- 10.3.4 Gaussian Process Regression Based on Feature Selection -- 10.4 Experiment Analysis -- References Electric power systems-Load dispatching Erscheint auch als Druck-Ausgabe Liu, Hui Non-Intrusive Load Monitoring Singapore : Springer Singapore Pte. Limited,c2020 9789811518591 |
| spellingShingle | Liu, Hui Non-Intrusive Load Monitoring Theory, Technologies and Applications Intro -- Preface -- Contents -- Nomenclature -- 1 Introduction -- 1.1 Overview of the Non-intrusive Load Monitoring -- 1.1.1 The Non-intrusive Load Monitoring -- 1.1.2 Overview of Recent Research in Non-intrusive Load Monitoring -- 1.2 Fundamental Key Problems of Non-intrusive Load Monitoring -- 1.2.1 Event Detection in Non-intrusive Load Monitoring -- 1.2.2 Feature Extraction in Non-intrusive Load Monitoring -- 1.2.3 Load Identification in Non-intrusive Load Monitoring -- 1.2.4 Energy Forecasting in Smart Buildings -- 1.3 Scope of This Book -- References -- 2 Detection of Transient Events in Time Series -- 2.1 Introduction -- 2.2 Cumulative Sum Based Transient Event Detection Algorithm -- 2.2.1 Mathematical Description of Change Point Detection -- 2.2.2 Parametric CUSUM Algorithm -- 2.2.3 Non-parametric CUSUM Algorithm -- 2.2.4 Sliding Windows Based on Two-Sided CUSUM Algorithm -- 2.2.5 Original Dataset -- 2.2.6 Evaluation Criteria and Results Analysis -- 2.3 Generalized Likelihood Ratio -- 2.3.1 The Theoretical Basis of GLR -- 2.3.2 Comparison of Event Detection Results -- 2.4 Sequential Probability Ratio Test -- 2.4.1 The Theoretical Basis of SPRT -- 2.4.2 Comparison of Event Detection Results -- 2.5 Experiment Analysis -- 2.5.1 The Results of Three Kinds of Algorithms -- 2.5.2 Conclusion -- References -- 3 Appliance Signature Extraction -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Feature Evaluation Indices -- 3.1.3 Classification Evaluation Indices -- 3.1.4 Data Selection -- 3.2 Features Based on Conventional Physical Definition -- 3.2.1 The Theoretical Basis of Physical Definition Features -- 3.2.2 Feature Extraction -- 3.2.3 Feature Evaluation -- 3.2.4 Classification Results -- 3.3 Features Based on Time-Frequency Analysis -- 3.3.1 The Theoretical Basis of Harmonic Features -- 3.3.2 Feature Extraction -- 3.3.3 Feature Evaluation 3.3.4 Classification Results -- 3.4 Features Based on VI Image -- 3.4.1 The Theoretical Basis of VI Image Features -- 3.4.2 Feature Extraction -- 3.4.3 Feature Evaluation -- 3.4.4 Classification Results -- 3.5 Features Based on Adaptive Methods -- 3.5.1 The Theoretical Basis of Adaptive Features -- 3.5.2 Feature Extraction -- 3.5.3 Classification Results -- 3.6 Experimental Analysis -- 3.6.1 Comparative Analysis of Classification Performance -- 3.6.2 Conclusion -- References -- 4 Appliance Identification Based on Template Matching -- 4.1 Introduction -- 4.1.1 Background -- 4.1.2 Data Preprocessing of the PLAID Dataset -- 4.2 Appliance Identification Based on Decision Tree -- 4.2.1 The Theoretical Basis of Decision Tree -- 4.2.2 Steps of Modeling -- 4.2.3 Classification Results -- 4.3 Appliance Identification Based on KNN Algorithm -- 4.3.1 The Theoretical Basis of KNN -- 4.3.2 Steps of Modeling -- 4.3.3 Classification Results -- 4.4 Appliance Identification Based on DTW Algorithm -- 4.4.1 The Theoretical Basis of DTW -- 4.4.2 Steps of Modeling -- 4.4.3 Classification Results -- 4.5 Experiment Analysis -- 4.5.1 Model Framework -- 4.5.2 Comparative Analysis of Classification Performance -- 4.5.3 Conclusion -- References -- 5 Steady-State Current Decomposition Based Appliance Identification -- 5.1 Introduction -- 5.2 Classical Steady-State Current Decomposition Models -- 5.2.1 Model Framework -- 5.2.2 Classical Features of Steady-State Decomposition and the Feature Extraction Method -- 5.2.3 Classical Methods in Steady-State Current Decomposition -- 5.2.4 Performance of the Various Features and Models -- 5.3 Current Decomposition Models Based on Harmonic Phasor -- 5.3.1 Model Framework -- 5.3.2 Novel Features of Steady-State Current Decomposition -- 5.3.3 Multi-objective Optimization Methods in Steady-State Current Decomposition 5.3.4 Performance of the Novel Features and Multi-objective Optimization Models -- 5.4 Current Decomposition Models Based on Non-negative Matrix Factor -- 5.4.1 Model Framework -- 5.4.2 Reconstruction of the Data -- 5.4.3 Non-negative Matrix Factorization Method of the Current Decomposition -- 5.4.4 Evaluation of the NMF Method in Current Decomposition -- 5.5 Experiment Analysis -- 5.5.1 Data Generation -- 5.5.2 Comparison Analysis of the Features Used in the Steady-State Decomposition -- 5.5.3 Comparison Analysis of the Models Used in the Steady-State Decomposition -- 5.5.4 Conclusion -- References -- 6 Machine Learning Based Appliance Identification -- 6.1 Introduction -- 6.2 Appliance Identification Based on Extreme Learning Machine -- 6.2.1 The Theoretical Basis of ELM -- 6.2.2 Steps of Modeling -- 6.2.3 Classification Results -- 6.3 Appliance Identification Based on Support Vector Machine -- 6.3.1 The Theoretical Basis of SVM -- 6.3.2 Steps of Modeling -- 6.3.3 Classification Results -- 6.4 Appliance Identification Based on Random Forest -- 6.4.1 The Theoretical Basis of Random Forest -- 6.4.2 Steps of Modeling -- 6.4.3 Classification Results -- 6.5 Experiment Analysis -- 6.5.1 Model Framework -- 6.5.2 Feature Preprocessing for Non-intrusive Load Monitoring -- 6.5.3 Classifier Model Optimization Algorithm for Non-intrusive Load Monitoring -- 6.6 Conclusion -- References -- 7 Hidden Markov Models Based Appliance -- 7.1 Introduction -- 7.2 Appliance Identification Based on Hidden Markov Models -- 7.2.1 Basic Problems Solved by HMM -- 7.2.2 Data Preprocessing -- 7.2.3 Determination of Load Status Information -- 7.3 Appliance Identification Based on Factorial Hidden Markov Models -- 7.3.1 The Theoretical Basis of the FHMM -- 7.3.2 Load Decomposition Steps Based on FHMM -- 7.3.3 Load Power Estimation -- 7.3.4 Decomposition Experiment Based on FHMM. 7.3.5 Evaluation Criteria and Result Analysis -- 7.4 Appliance Identification Based on Hidden Semi-Markov Models -- 7.4.1 Hidden Semi-Markov Model -- 7.4.2 Improved Viterbi Algorithm -- 7.4.3 Evaluation Criteria and Result Analysis -- 7.5 Experiment Analysis -- References -- 8 Deep Learning Based Appliance Identification -- 8.1 Introduction -- 8.1.1 Deep Learning -- 8.1.2 NILM Based on Deep Learning -- 8.2 Appliance Identification Based on End-to-End Decomposition -- 8.2.1 Single Feature Based LSTM Network Load Decomposition -- 8.2.2 Multiple Features Based LSTM Network Load Decomposition -- 8.3 Appliance Identification Based on Appliance Classification -- 8.3.1 Appliance Identification Based on CNN -- 8.3.2 Appliance Identification Based on AlexNet -- 8.3.3 Appliance Identification Based on LeNet-SVM Model -- 8.4 Experiment Analysis -- 8.4.1 Experimental Analysis of End-to-End Decomposition -- 8.4.2 Experimental Analysis of Appliance Classification -- References -- 9 Deterministic Prediction of Electric Load Time Series -- 9.1 Introduction -- 9.1.1 Background -- 9.1.2 Advance Prediction Strategies -- 9.1.3 Original Electric Load Time Series -- 9.2 Load Forecasting Based on ARIMA Model -- 9.2.1 Model Framework -- 9.2.2 Theoretical Basis of ARIMA -- 9.2.3 Modeling Steps of ARIMA Predictive Model -- 9.2.4 Predictive Results -- 9.2.5 The Theoretical Basis of EMD -- 9.2.6 Optimization of EMD Decomposition Layers -- 9.2.7 Predictive Results -- 9.3 Load Forecasting Based on Elman Neural Network -- 9.3.1 Model Framework -- 9.3.2 Steps of Modeling -- 9.3.3 Predictive Results -- 9.3.4 Optimization of EMD Decomposition Layers -- 9.3.5 Predictive Results -- 9.4 Experiment Analysis -- 9.4.1 Comparative Analysis of Predictive Performance -- 9.5 Conclusion -- References -- 10 Interval Prediction of Electric Load Time Series -- 10.1 Introduction 10.2 Interval Prediction Based on Quantile Regression -- 10.2.1 The Performance Evaluation Metrics -- 10.2.2 Original Sequence for Modeling -- 10.2.3 The Theoretical Basis of Quantile Regression -- 10.2.4 Quantile Regression Based on the Total Electric Load Time Series -- 10.2.5 Quantile Regression Based on Additional Time and Date Information -- 10.2.6 Quantile Regression Based on Information from Sub-meters -- 10.3 Interval Prediction Based on Gaussian Process Filtering -- 10.3.1 The Theoretical Basis of Gaussian Process Regression -- 10.3.2 Gaussian Process Regression Based on the Total Electric Load Time Series -- 10.3.3 Gaussian Process Regression Based on Different Input Features -- 10.3.4 Gaussian Process Regression Based on Feature Selection -- 10.4 Experiment Analysis -- References Electric power systems-Load dispatching |
| title | Non-Intrusive Load Monitoring Theory, Technologies and Applications |
| title_auth | Non-Intrusive Load Monitoring Theory, Technologies and Applications |
| title_exact_search | Non-Intrusive Load Monitoring Theory, Technologies and Applications |
| title_exact_search_txtP | Non-Intrusive Load Monitoring Theory, Technologies and Applications |
| title_full | Non-Intrusive Load Monitoring Theory, Technologies and Applications |
| title_fullStr | Non-Intrusive Load Monitoring Theory, Technologies and Applications |
| title_full_unstemmed | Non-Intrusive Load Monitoring Theory, Technologies and Applications |
| title_short | Non-Intrusive Load Monitoring |
| title_sort | non intrusive load monitoring theory technologies and applications |
| title_sub | Theory, Technologies and Applications |
| topic | Electric power systems-Load dispatching |
| topic_facet | Electric power systems-Load dispatching |
| work_keys_str_mv | AT liuhui nonintrusiveloadmonitoringtheorytechnologiesandapplications |