Machine learning approaches to non-intrusive load monitoring. ([2020])

• Record Type: Book
• Further Information: Note: Roberto Bonfigli, Stefano Squartini.
• Authors: Bonfigli, Roberto; Squartini, Stefano
• Contents: 1 Introduction 2 Non-Intrusive Load Monitoring 2.1 Problem statement 2.2 State of the Art 2.3 Datasets 2.4 Evaluation metrics 2.5 Remarks 3 Background 3.1 Hidden Markov Model (HMM) 3.1.1 Baum-Welch algorithm 3.1.2 Factorial HMM 3.2 Deep Neural Network (DNN) 3.2.1 Stochastic gradient descent (SGD) 3.2.2 Autoencoder 4 HMM based approach 4.1 Additive Factorial Approximate Maximum A-Posteriori (AFAMAP) 4.1.1 Appliance modelling 4.1.2 Rest-of-the-World model 4.2 Algorithm improvements 4.2.1 Experimental setup 4.2.2 Results 4.3 Exploitation of the reactive power 4.3.1 AFAMAP formulation 4.3.2 Experimental setup 4.3.3 Results 4.4 Footprint extraction procedure 4.4.1 Experimental setup 4.4.2 Results 5 DNN based approach 5.1 Neural NILM 5.2 Denoising AutoEncoder approach 5.3 Algorithm improvements 5.3.1 Experimental setup 5.3.2 Results 5.4 Exploitation of the reactive power 5.4.1 Experimental setup 5.4.2 Results 6 Conclusions 6.1 Future Research Topics 7 References.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2020
• Extent: 1 online resource (viii, 135 pages), illustrations (some color)
• Subjects: 006.3/1; Machine learning; Electronic books
• Languages: English
• ISBNs: 9783030307820; 3030307824
• Related ISBNs: 9783030307813
• Notes: Note: Includes bibliographical references and index.; Note: Online resource; title from PDF title page (SpringerLink, viewed November 6, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.