Energy Management Using Real-Time Non-Intrusive Load Monitoring
Non-Intrusive Load Monitoring Toolkit (nilmtk)
A repository of awesome Non-Intrusive Load Monitoring(NILM) with code.
An archive for NILM papers with source code and other supplemental material
Overview of research papers with focus on low frequency NILM employing DNNs
Smart home-IoT ecosystem for Active Ambient Assisted Living
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 contains the energy disaggregation code based on Graph Signal Processing approach
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 …
Overview of NILM works employing Deep Neural Networks on low frequency data
DEPS: Dataset de la Escuela Politénica Superior
Undergraduate research by Yuzhe Lim in Spring 2019. Field of research: Deep Neural Networks application on NILM (Nonintrusive load monitoring) for Energy Disaggregation
Multi-NILM: Multi Label Non Intrusive Load Monitoring
This repository contains assignments and project work related to the course
This repository contains my implementation for Energy Disaggregation of appliances from mains consumption using stacked ensemble deep learning
This repo provides four weight pruning algorithms for use in sequence-to-point energy disaggregation as well as three alternative neural network architectures.
A Synthetic Energy Consumption Dataset for Non-Intrusive Load Monitoring
🔌 Load Monitoring and Energy Disaggregation on a RasPi
Metrics to assess the generalisation ability of NILM algorithms
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