NILM applications: Literature review of learning approaches, recent developments and challenges

GF Angelis, C Timplalexis, S Krinidis, D Ioannidis… - Energy and …, 2022 - Elsevier
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 …

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 …

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 …

Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets

CCM Yeh, Y Zhu, L Ulanova, N Begum… - 2016 IEEE 16th …, 2016 - ieeexplore.ieee.org
The all-pairs-similarity-search (or similarity join) problem has been extensively studied for
text and a handful of other datatypes. However, surprisingly little progress has been made …

Short-term residential load forecasting based on resident behaviour learning

W Kong, ZY Dong, DJ Hill, F Luo… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Residential load forecasting has been playing an increasingly important role in modern
smart grids. Due to the variability of residents' activities, individual residential loads are …

An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

D Murray, L Stankovic, V Stankovic - Scientific data, 2017 - nature.com
Smart meter roll-outs provide easy access to granular meter measurements, enabling
advanced energy services, ranging from demand response measures, tailored energy …

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 …

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 …