Non-Intrusive Load Monitoring Toolkit (nilmtk)

Deep Neural Networks Applied to Energy Disaggregation

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.

Energy Management Using Real-Time Non-Intrusive Load Monitoring

Multi-NILM: Multi Label Non Intrusive Load Monitoring

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

⚠️ This repository is no longer actively maintained.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.

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 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

AMBAL-based NILM Trace generator

Energy disaggregation - Deep learning approach.

An Open Source Grid Intelligence Platform for Everyone

Scripts of the the Rainforest Automation Energy Dataset (RAE dataset)

NILM performance evaluation functions use in the Springer Energy Efficiency journal paper.

Mixed-Integer Nonlinear Programming for NILM

Importing script to data wrangle (convert, clean, and repair data from) the REDD dataset

Supervised NILM using multiple-choice knapsack problem (MCKP).

Non-Intrusive Load Monitoring Device

This repository is the code basis for the paper titled "Using Deep Learning and Knowledge Transfer to Disaggregate Energy Consumption"