Machine Learning Approaches to Non Intrusive Load Monitoring 1st Edition by Roberto Bonfigli, Stefano Squartini ISBN 3030307816 978-3030307813

Machine Learning Approaches to Non Intrusive Load Monitoring 1st Edition by Roberto Bonfigli, Stefano Squartini- Ebook PDF Instant Download/Delivery: 3030307816, 978-3030307813

Full download Machine Learning Approaches to Non Intrusive Load Monitoring 1st Edition after payment

Product details:

ISBN 10: 3030307816 

ISBN 13: 978-3030307813

Author: Roberto Bonfigli, Stefano Squartini

  Research on Smart Grids has recently focused on the energy monitoring issue, with the objective of maximizing the user consumption awareness in building contexts on the one hand, and providing utilities with a detailed description of customer habits on the other. In particular, Non-Intrusive Load Monitoring (NILM), the subject of this book, represents one of the hottest topics in Smart Grid applications. NILM refers to those techniques aimed at decomposing the consumption-aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the electrical system under study. 
This book provides a status report on the most promising NILM methods, with an overview of the publically available dataset on which the algorithm and experiments are based. Of the proposed methods, those based on the Hidden Markov Model (HMM) and the Deep Neural Network (DNN) are the best performing and most interesting from the future improvement point of view. One method from each category has been selected and the performance improvements achieved are described. Comparisons are made between the two reference techniques, and pros and cons are considered. In addition, performance improvements can be achieved when the reactive power component is exploited in addition to the active power consumption trace.

Table of contents: 

1. Introduction

2. Non-intrusive Load Monitoring

3. Background

4. HMM Based Approach

5. DNN Based Approach

6. Conclusions

People also search for:

 machine learning approaches to non intrusive load monitoring
    
machine learning intrusion detection
    
non-intrusive load monitoring
    
monitoring machine learning
    
machine learning intrusion detection system

Tags: Roberto Bonfigli, Stefano Squartini, Machine, Learning, Approaches, Non Intrusive, Load, Monitoring