Abstract
We developed a machine learning-based load disaggregation method to estimate the real-time output of individual appliances from the whole-house measurements. We first learn the important features associated with each type of appliances using the ground truth consumption data of individual appliances potentially available for a small set of houses equipped with submeters. The learned features are then be used to estimate the power generation/consumption of the appliances from the whole-house consumption. This developed load disaggregation software includes two steps. The first step is to identify the on/off status of different appliances using a classification method, and the second step is to estimate the appliance output using a regression method.
- Release Date:
- 2022-04-05
- Project Type:
- Closed Source
- Software Type:
- Scientific
- Sponsoring Org.:
-
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies OfficePrimary Award/Contract Number:AC36-08GO28308
- Code ID:
- 73278
- Site Accession Number:
- SWR-22-33
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Hao, Jun, and Yang, Rui.
Load Disaggregation (Modeling Individual Appliance Power Generation & Consumption in Real-Time).
Computer Software.
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office.
05 Apr. 2022.
Web.
doi:10.11578/dc.20220826.2.
Hao, Jun, & Yang, Rui.
(2022, April 05).
Load Disaggregation (Modeling Individual Appliance Power Generation & Consumption in Real-Time).
[Computer software].
https://doi.org/10.11578/dc.20220826.2.
Hao, Jun, and Yang, Rui.
"Load Disaggregation (Modeling Individual Appliance Power Generation & Consumption in Real-Time)." Computer software.
April 05, 2022.
https://doi.org/10.11578/dc.20220826.2.
@misc{
doecode_73278,
title = {Load Disaggregation (Modeling Individual Appliance Power Generation & Consumption in Real-Time)},
author = {Hao, Jun and Yang, Rui},
abstractNote = {We developed a machine learning-based load disaggregation method to estimate the real-time output of individual appliances from the whole-house measurements. We first learn the important features associated with each type of appliances using the ground truth consumption data of individual appliances potentially available for a small set of houses equipped with submeters. The learned features are then be used to estimate the power generation/consumption of the appliances from the whole-house consumption. This developed load disaggregation software includes two steps. The first step is to identify the on/off status of different appliances using a classification method, and the second step is to estimate the appliance output using a regression method.},
doi = {10.11578/dc.20220826.2},
url = {https://doi.org/10.11578/dc.20220826.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20220826.2}},
year = {2022},
month = {apr}
}