This dataset imbalance visualization toolkit will be the beginning of a fire-new branch in NILM studies. (the website is pending)
Monitoramento Não-Intrusivo de Carga Elétrica e Identificação de Consumidores Individuais
The AMBAL-based NILM Trace generator (for NILMTK)
In this project, we've tried applying various DNNs to the problem of non-intrusive load monitoring (NILM) and compared their results for various appliances using the REDD dataset. We took a sliding window approach in hopes that we'll be able to achieve real time disaggregation with further tuning and testing. We compare the disaggregated energy …
Non Intrusive Load Monitoring
The code to reproduce all the numerical results and the plots of the paper.
Non-Intrusive Load Monitoring Device
An Open Source Grid Intelligence Platform for Everyone
Easy to implement and customize an end-to-end machine learning pipeline for training the archiecture of seq2point model for energy disaggregation. Run experiments and analyze training procedures or models with ease.
COMMIT-NILM: COMputational MonItoring Tool for NILM Algorithms
ST-NILM is a new integrated architecture based on the Scattering Transform. It has a DCN (Deep Convolutional Network) with analytical wavelet-based non-trained weights, shared with fully connected output networks that perform event detection and multi-label classification of aggregate loads.
Deep Neural Networks for Nonintrusive Load Monitoring (Energy Disaggregation)
PSB for the connection between the ESP32-WROOM-32 and a Solid State Relay. In adition, there is a temperature sensor
Master's Dissertation: Unsupervised Low-Frequency NILM for Industrial Loads
uDisagg is an optimized real-time embedded low-frequency load disaggregation algorithm (or disaggregator). This project contains the source code for my PhD thesis experimentation.
The C++ code for my Computing MSc energy disaggregation project
Supplemental material on comparability and performance evaluation in NILM
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