The AMBAL-based NILM Trace generator (for NILMTK)
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Updated
Aug 25, 2021 - Python
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 …
This repository contains assignments and project work related to the course
Deep Neural Networks for Nonintrusive Load Monitoring (Energy Disaggregation)
To view this presentation in your browser, go to:
Overview of NILM works employing Deep Neural Networks on low frequency data
Code for our MPS 2019 paper entitled "A Machine Learning Approach for NILM based on Odd Harmonic Current Vectors"
Metrics to assess the generalisation ability of NILM algorithms
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).
This repository contains my implementation for Energy Disaggregation of appliances from mains consumption using stacked ensemble deep learning
Non Intrusive Load Monitoring data repository and data converter for NILMTK
Slides for my talk on "Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature"
A schema for modelling meters, measurements, appliances, buildings etc
DEPS: Dataset de la Escuela Politénica Superior
Supplemental material on comparability and performance evaluation in NILM
Presentation of Neural NILM for BuildSys 2015 conference in November 2015
Overview of research papers with focus on low frequency NILM employing DNNs
Undergraduate research by Yuzhe Lim in Spring 2019. Field of research: Deep Neural Networks application on NILM (Nonintrusive load monitoring) for Energy Disaggregation
Machine Learning and Internet of Things approach for turning off appliances when not used for saving power consumption.
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