Non-Intrusive Load Monitoring Toolkit (nilmtk)
python energy algorithms ipython-notebook forecasting disaggregation nilm energy-disaggregation nilmtk nilm-algorithms
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- Python
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nilm
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A curated resources of awesome NILM resources
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Deep Neural Networks Applied to Energy Disaggregation
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- Python
Code for NILM experiments using Neural Networks. Uses Keras/Tensorflow and the NILMTK.
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- Jupyter Notebook
A repository of awesome Non-Intrusive Load Monitoring(NILM) with code.
awesome deep-learning time-series pytorch nilm energy-disaggregation nilm-algorithms electricity blind-source-separation smart-grid non-intrusive-load-monitoring paper-with-code
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Energy Management Using Real-Time Non-Intrusive Load Monitoring
deep-learning nilm energy-disaggregation edge-computing energy-management tensorflow-lite tensorflow2
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- Python
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.
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- Python
Multi-NILM: Multi Label Non Intrusive Load Monitoring
time-series dft sax boss nilm energy-disaggregation multi-label sfa blind-source-separation weasel paa time-series-representations non-intrusive-load-monitoring multi-nilm signal2vec multi-label-nilm time-series-embedding 1d-sax
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- Python
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- Jupyter Notebook
An Attention-based Deep Neural Network for Non-Intrusive Load Monitoring
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- Python
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.
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- Jupyter Notebook
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.
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- Python
This repository is no longer actively maintained. It previously dealt with Non-Intrusive Load Monitoring (NILM), focusing on predicting household appliance status from aggregated power load data. We explored different thresholding methods and evaluated deep learning models for regression and classification tasks.
monitoring deep-learning neural-network tensorflow pytorch recurrent-neural-networks gru nilm nilm-algorithms convolutional-neural-networks thresholding domestic-appliances thresholding-methods
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- Python
[KDD 2025] NILMFormer: A Sequence-To-Sequence Non-Stationarity Aware Transformer for Non-Intrusive Load Monitoring
deep-learning transformer nilm electricity-consumption timeseries-analysis non-stationary smart-meter-data
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- Python
A reimplementation of Jack Kelly's rectangles neural network architecture based on Keras and the NILMToolkit.
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- Python
A Synthetic Energy Consumption Dataset for Non-Intrusive Load Monitoring
nilm energy-disaggregation nilmtk datasets energy-consumption datasets-simulation non-intrusive-load-monitoring
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Undergraduate research by Yuzhe Lim in Spring 2019. Field of research: Deep Neural Networks application on NILM (Nonintrusive load monitoring) for Energy Disaggregation
machine-learning deep-neural-networks deep resnet nilm energy-disaggregation denoising-autoencoders lstm-neural-networks
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- Jupyter Notebook
An ESP32 based plug level electricity meter
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- C++
In this repository are available codes in python for implementation of classification of loads and event detection using PLAID dataset
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- Python
Electrical Devices Identification Model (EDIM) for the identification of electrical devices by analyzing their energy consumption profiles.
machine-learning signal-processing feature-extraction nilm nilm-algorithms python-3 feature-engineering electrical colab-notebook
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- Jupyter Notebook
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