Non-Intrusive Load Monitoring Toolkit (nilmtk)

A curated resources of awesome NILM resources

Deep Neural Networks Applied to Energy Disaggregation

Code for NILM experiments using Neural Networks. Uses Keras/Tensorflow and the NILMTK.

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.

A repository of awesome Non-Intrusive Load Monitoring(NILM) with code.

Multi-NILM: Multi Label Non Intrusive Load Monitoring

Energy Management Using Real-Time Non-Intrusive Load Monitoring

An Attention-based Deep Neural Network for Non-Intrusive Load Monitoring

A reimplementation of Jack Kelly's rectangles neural network architecture based on Keras and the NILMToolkit.

⚠️ This repository is no longer actively maintained.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.

Undergraduate research by Yuzhe Lim in Spring 2019. Field of research: Deep Neural Networks application on NILM (Nonintrusive load monitoring) for Energy Disaggregation

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 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.

A Synthetic Energy Consumption Dataset for Non-Intrusive Load Monitoring

Overview of research papers with focus on low frequency NILM employing DNNs

In this repository are available codes in python for implementation of classification of loads and event detection using PLAID dataset

An ESP32 based plug level electricity meter