nilm

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.

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

Multi-NILM: Multi Label Non Intrusive Load Monitoring

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

A Synthetic Energy Consumption Dataset for Non-Intrusive Load Monitoring

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

Non-Intrusive Load Monitoring (NILM) aims to predict the status or consumption of domestic appliances in a household only by knowing the aggregated power load. NILM can be formulated as regression problem or most often as a classification problem. Most datasets gathered by smart meters allow to define naturally a regression problem, but the corresponding classification problem is a derived one, since it requires a conversion from the power signal to the status of each device by a thresholding method. We treat three different thresholding methods to perform this task, discussing their differences on various devices from the UK-DALE dataset. We analyze the performance of deep learning state-of-the-art architectures on both the regression and classification problems, introducing criteria to select the most convenient thresholding method.

AMBAL-based NILM Trace generator

Energy disaggregation - Deep learning approach.

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

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

An aided linear integer programming (ALIP) non-intrusive load monitoring (NILM) algorithm.

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

Presentation of Neural NILM for BuildSys 2015 conference in November 2015

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

Electrical Devices Identification Model (EDIM) for the identification of electrical devices by analyzing their energy consumption profiles.

Non Intrusive Load Monitoring data repository and data converter for NILMTK

COMMIT-NILM: COMputational MonItoring Tool for NILM Algorithms

Code for our MPS 2019 paper entitled "A Machine Learning Approach for NILM based on Odd Harmonic Current Vectors"

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

The code to reproduce all the numerical results and the plots of the paper.

Overview of NILM works employing Deep Neural Networks on low frequency data

A Moroccan Buildings’ Electricity Consumption Dataset. MORED is made available by TICLab of the International University of Rabat (UIR), and the data collection was carried out as part of PVBuild research project, coordinated by Prof. Mounir Ghogho and funded by the United States Agency for International Development (USAID).

Scripts and things used to create the AMPds dataset.