Non-Intrusive Load Monitoring Toolkit (nilmtk)

Deep Neural Networks Applied to Energy Disaggregation

An archive for NILM papers with source code and other supplemental material

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

Energy Management Using Real-Time Non-Intrusive Load Monitoring

Multi-NILM: Multi Label Non Intrusive Load Monitoring

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This repo provides four weight pruning algorithms for use in sequence-to-point energy disaggregation as well as three alternative neural network architectures.

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

This contains the energy disaggregation code based on Graph Signal Processing approach

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

🔌 Load Monitoring and Energy Disaggregation on a RasPi

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).

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

A User-Oriented Energy Monitor to Enhance Energy Efficiency in Households

Non Intrusive Load Monitoring data repository and data converter for NILMTK

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

Supplemental material on comparability and performance evaluation in NILM

Presentation of Neural NILM for BuildSys 2015 conference in November 2015

DEPS: Dataset de la Escuela Politénica Superior

Metrics to assess the generalisation ability of NILM algorithms

Machine Learning and Internet of Things approach for turning off appliances when not used for saving power consumption.

In this project, we've tried applying various DNNs to the problem of non-intrusive load monitoring (NILM) and compared their results for various appliances using the REDD dataset. We took a sliding window approach in hopes that we'll be able to achieve real time disaggregation with further tuning and testing. We compare the disaggregated energy consumption results based on MSE, MAE, Relative Error and F1 Score.

This repository contains my implementation for Energy Disaggregation of appliances from mains consumption using stacked ensemble deep learning

Dataset for Industrial Energy Disaggregation (NILM)

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This repository contains assignments and project work related to the course

Slides for my talk on "Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature"

Non-intrusive Load Monitoring