Non-Intrusive Load Monitoring Toolkit (nilmtk)
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Updated
Apr 23, 2024 - Python
Non-Intrusive Load Monitoring Toolkit (nilmtk)
Deep Neural Networks Applied to Energy Disaggregation
Code for NILM experiments using Neural Networks. Uses Keras/Tensorflow and the NILMTK.
A curated resources of awesome NILM resources
The super-state hidden Markov model disaggregator that uses a sparse Viterbi algorithm for decoding. This project contains the source code that was use for my IEEE Transactions on Smart Grid journal paper.
Multi-NILM: Multi Label Non Intrusive Load Monitoring
A repository of awesome Non-Intrusive Load Monitoring(NILM) with code.
An Attention-based Deep Neural Network for Non-Intrusive Load Monitoring
Energy Management Using Real-Time Non-Intrusive Load Monitoring
Simple, fast and handy data loaders for NILM datasets to explore the data at convenience, provided with basic transformations like resampling, normalization and extract activities by thresholding.
A Synthetic Energy Consumption Dataset for Non-Intrusive Load Monitoring
AMBAL-based NILM Trace generator
Supervised NILM using multiple-choice knapsack problem (MCKP).
Group of Apprentices to learn NILMTK with UK-DALE
A reimplementation of Jack Kelly's rectangles neural network architecture based on Keras and the NILMToolkit.
In this repository are available codes in python for implementation of classification of loads and event detection using PLAID dataset
A new CNN architecture to perform detection, feature extraction, and multi-label classification of loads, in non-intrusive load monitoring (NILM) approaches, with a single model for high-frequency signals.
Overview of research papers with focus on low frequency NILM employing DNNs
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