load-disaggregation

Non-Intrusive Load Monitoring (NILM) for Home Assistant. Estimate appliance activity and energy usage from a whole-home power sensor, all locally.

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).

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 …

Non-Intrusive Load Monitoring (NILM) via seq2point regression. Comparing a 1D-CNN and Decision Tree with interpretability via Grad-CAM and decision paths

Reproducible NILM pipeline for REFIT household electricity data, covering data cleaning, appliance usage analysis, machine learning disaggregation, and utility energy insights.

CT-based load disaggregation for Home Assistant: infer per-device power from the current transformers you already have, with honest confidence tiers and an explicit unknown residual.