[PDF] Top 20 Non-intrusive load disaggregation using graph signal processing
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Non-intrusive load disaggregation using graph signal processing
... a graph signal processing (GSP) method for steady-state NILM to address the large training overhead and associated complexity of conventional graph-based methods through a novel event- based ... See full document
9
On a training-less solution for non-intrusive appliance load monitoring using graph signal processing
... features using a model built during the training ...appliance load model using a finite state machine by learning parameters for prior distributions of appliance ... See full document
16
A generic optimisation-based approach for improving non-intrusive load monitoring
... the disaggregation results by matching the falling and rising edges obtained by edge ...a graph is generated and according to minimisation of graph signal total variation all possible ... See full document
9
Can non-intrusive load monitoring be used for identifying an appliance's anomalous behaviour?
... i.e., using separate energy monitors for each appliance, to de- tect appliance-specific faulty behaviour is neither a scalable nor practical solu- ...or non-intrusive load ... See full document
32
Subtask Gated Networks for Non-Intrusive Load Monitoring
... energy disaggregation algorithm based on a conditional factorial hidden semi-Markov model, which exploits addi- tional features related to when and how appliances are used (Kim et ...difference signal and ... See full document
8
Non-intrusive load monitoring under residential solar power influx
... authors using a KLE based low-resolution spectral decomposition technique has been discussed in ...supervised Graph Signal Processing (GSP) based NILM method has been ...method using ... See full document
33
Web interactive non intrusive load disaggregation system for active demand in smart grids
... Nevertheless, the major difficulty is the lack of information about day-to-day activities; for instance, energy bills, which are usually received at the end of each month, cannot be used to distinguish the effects of ... See full document
9
A graph-based signal processing approach for low-rate energy disaggregation
... image processing to wireless sensor ...specifically, graph Fourier transform, has been used for image compression (depth map coding) and image denoising in [19] and [20], ...classification, using an ... See full document
7
Blind non-intrusive appliance load monitoring using graph-based signal processing
... deployments, disaggregation of household’s total energy con- sumption down to individual appliances using purely software tools, ...aka. non-intrusive appliance load monitoring (NALM), ... See full document
5
Blind non-intrusive appliance load monitoring using graph-based signal processing
... [3] J. Liao, L. Stankovic, and V. Stankovic, ”Detecting household activity patterns from smart meter data,” IE-2014 10th IEEE International Conference on Intelligent Environments, Shanghai, China, July 2014. [4] C. ... See full document
5
Networked Data Analytics: Network Comparison And Applied Graph Signal Processing
... The two main techniques to design collaborative filtering algorithms are nearest neighbors (NN) estimators and latent linear factor (LF) models. User-based NN schemes work under the assump- tion that users who are ... See full document
258
Off-the-shelf Non-Intrusive Load Monitoring Devices Utilised in a Low Activity Detection Service
... [r] ... See full document
11
Off-the-shelf Non-Intrusive Load Monitoring Devices Utilised in a Low Activity Detection Service
... One method of monitoring that shows promise for being low intrusion is the use of electricity consumption data (Clement, Ploennigs, & Kabitzsch, 2012). A considerable amount of information can be gathered from ... See full document
7
Unsupervised training methods for non-intrusive appliance load monitoring from smart meter data
... In contrast to the technical challenges described so far, a number of social barriers have also prevented progress in the field of NIALM. One such barrier arises due to the in- trinsic privacy concerns related to NIALM ... See full document
116
Energy saving through voltage optimisation & non intrusive load monitoring in domestic house
... The approach introduced in (Marceau & Zmeureanu 2000) is somehow similar to the one introduced in (Powers et al. 1991). Although it samples and calculate the real power at 16s intervals, it also takes account of the ... See full document
213
Case Study on Graph Processing Using Graph Engine
... Data processing in a computing device is the result of efficient query processing and optimization of ...When using real life applications of using an abstract data type, a graph is the ... See full document
5
Non-intrusive load management system for residential loads using artificial neural network based arduino microcontroller
... The NILM system can be applied in many energy consuming systems to facilitate energy management. For instance, the NILM system can determine the load schedule in an aircraft or motor vehicle from the measurement ... See full document
45
Signal Processing of ECG Using Matlab
... biomedical signal in the present work is the ECG signal and the filtering technique suggested is Butterworth filter or simply FIR Type-1 ...filters using signal processing techniques or ... See full document
6
Deep Learning on Smart Meter Data: Non-Intrusive Load Monitoring and Stealthy Black-Box Attacks
... respondents knew how many kWh they used per month or per day and most of them did not even know where their electricity meter was located. This lack of awareness can cause ine ffi - cient power usage for the sake of ... See full document
83
Radiofrequency analysis using optical signal processing
... This ratio is of the order of 10"^, so that a practical radiofrequency spectrum analyser could not be based on a fixed cavity using this configuration. However, if one of the mirrors is repeatedly cycled ... See full document
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