Energy disaggregation via deep temporal dictionary learning

M Khodayar, J Wang, Z Wang - IEEE transactions on neural …, 2019 - ieeexplore.ieee.org
This paper presents a novel nonlinear dictionary learning (DL) model to address the energy
disaggregation (ED) problem, ie, decomposing the electricity signal of a home to its …

Power disaggregation of combined HVAC loads using supervised machine learning algorithms

I Rahman, M Kuzlu, S Rahman - Energy and Buildings, 2018 - Elsevier
Power disaggregation algorithms are used to decompose building level power consumption
data into individual equipment level power information. In order to ensure energy efficient …

Structured dictionary learning for energy disaggregation

S Pandey, G Karypis - Proceedings of the Tenth ACM International …, 2019 - dl.acm.org
The increased awareness regarding the impact of energy consumption on the environment
has led to an increased focus on reducing energy consumption. Feedback on the appliance …

A study of the communication needs in micro-grid systems

C Mavrokefalidis, D Ampeliotis… - 2017 XXXIInd General …, 2017 - ieeexplore.ieee.org
The demand for increased power delivery, the extensive incorporation of distributed
generators, such as the ones utilizing renewable energy sources, and the adoption of …

Electrical Load Disaggregation and Demand Response in Commercial Buildings

I Rahman - 2020 - vtechworks.lib.vt.edu
Electrical power systems consist of a large number of power generators connected to
consumers through a complex system of transmission and distribution lines. Within the …

Decomposing Residential Monthly Electric Utility Bill Into HVAC Energy Use Using Machine Learning

SS Yakkali - 2019 - search.proquest.com
About 38% of total energy consumption in the US can be attributed to residential usage,
48% of which is consumed by Heating, Ventilation and Air Conditioning (HVAC) systems …