[HTML][HTML] A review on buildings energy information: Trends, end-uses, fuels and drivers

M González-Torres, L Pérez-Lombard, JF Coronel… - Energy Reports, 2022 - Elsevier
Buildings are a major contributor to climate change, accounting for one third of global
energy consumption and one quarter of CO 2 emissions. However, comprehensive …

[HTML][HTML] Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

I Antonopoulos, V Robu, B Couraud, D Kirli… - … and Sustainable Energy …, 2020 - Elsevier
Recent years have seen an increasing interest in Demand Response (DR) as a means to
provide flexibility, and hence improve the reliability of energy systems in a cost-effective way …

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 …

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 …

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 …

Measures to improve energy demand flexibility in buildings for demand response (DR): A review

Y Chen, P Xu, J Gu, F Schmidt, W Li - Energy and buildings, 2018 - Elsevier
This paper classifies and discusses the energy flexibility improvement strategies for demand
responsive control in grid-interactive buildings based on a comprehensive study of the …

[HTML][HTML] 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 …

Transfer learning for non-intrusive load monitoring

M D'Incecco, S Squartini… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only
the recorded mains in a household. NILM is unidentifiable and thus a challenge problem …

Key management systems for smart grid advanced metering infrastructure: A survey

A Ghosal, M Conti - IEEE Communications Surveys & Tutorials, 2019 - ieeexplore.ieee.org
Smart grids are evolving as the next generation power systems that transform the traditional
ways of functioning of present electrical grids. Advanced metering infrastructure (AMI) is one …

A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed

X Xu, H Yu, Q Sun, VWY Tam - Renewable and Sustainable Energy …, 2023 - Elsevier
Occupant behavior has been widely considered as one of the key influencing factors on
building energy consumption. The complexity of its formation mechanism and the dynamic …