[HTML][HTML] Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …
installed in residential buildings. If leveraged properly, that data could assist end-users …
NILM applications: Literature review of learning approaches, recent developments and challenges
This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem,
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
Non-intrusive load monitoring: A review
PA Schirmer, I Mporas - IEEE Transactions on Smart Grid, 2022 - ieeexplore.ieee.org
The rapid development of technology in the electrical energy sector within the last 20 years
has led to growing electric power needs through the increased number of electrical …
has led to growing electric power needs through the increased number of electrical …
Energy management using non-intrusive load monitoring techniques–State-of-the-art and future research directions
R Gopinath, M Kumar, CPC Joshua… - Sustainable Cities and …, 2020 - Elsevier
In recent years, the development of smart sustainable cities has become the primary focus
among urban planners and policy makers to make responsible use of resources, conserve …
among urban planners and policy makers to make responsible use of resources, conserve …
An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study
Smart meter roll-outs provide easy access to granular meter measurements, enabling
advanced energy services, ranging from demand response measures, tailored energy …
advanced energy services, ranging from demand response measures, tailored energy …
Review on deep neural networks applied to low-frequency nilm
P Huber, A Calatroni, A Rumsch, A Paice - Energies, 2021 - mdpi.com
This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep
neural networks to disaggregate appliances from low frequency data, ie, data with sampling …
neural networks to disaggregate appliances from low frequency data, ie, data with sampling …
Neural nilm: Deep neural networks applied to energy disaggregation
J Kelly, W Knottenbelt - Proceedings of the 2nd ACM international …, 2015 - dl.acm.org
Energy disaggregation estimates appliance-by-appliance electricity consumption from a
single meter that measures the whole home's electricity demand. Recently, deep neural …
single meter that measures the whole home's electricity demand. Recently, deep neural …
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
J Kelly, W Knottenbelt - Scientific data, 2015 - nature.com
Many countries are rolling out smart electricity meters. These measure a home's total power
demand. However, research into consumer behaviour suggests that consumers are best …
demand. However, research into consumer behaviour suggests that consumers are best …
Towards trustworthy energy disaggregation: A review of challenges, methods, and perspectives for non-intrusive load monitoring
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power
consumption into its individual sub-components. Over the years, signal processing and …
consumption into its individual sub-components. Over the years, signal processing and …
Smart meter data privacy: A survey
Automated and smart meters are devices that are able to monitor the energy consumption of
electricity consumers in near real-time. They are considered key technological enablers of …
electricity consumers in near real-time. They are considered key technological enablers of …