energy-disaggregation

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

Multi-NILM: Multi Label Non Intrusive Load Monitoring

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

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

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

🔌 Load Monitoring and Energy Disaggregation on a RasPi

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

Presentation of Neural NILM for BuildSys 2015 conference in November 2015

Supplemental material on comparability and performance evaluation in NILM

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

Non Intrusive Load Monitoring data repository and data converter for NILMTK

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

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.

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).

Minion - World's Smallest AI Energy Auditor

DEPS: Dataset de la Escuela Politénica Superior

To view this presentation in your browser, go to:

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

Code completed by group for course in energy analytics

The AMBAL-based NILM Trace generator (for NILMTK)

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.

A schema for modelling meters, measurements, appliances, buildings etc

This repository contains assignments and project work related to the course