Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
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 approaches for disaggregated energy sensing: A survey
Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing
them to obtain appliance-specific energy consumption statistics that can further be used to …
them to obtain appliance-specific energy consumption statistics that can further be used to …
Sequence-to-point learning with neural networks for non-intrusive load monitoring
Energy disaggregation (aka nonintrusive load monitoring, NILM), a single-channel blind
source separation problem, aims to decompose the mains which records the whole house …
source separation problem, aims to decompose the mains which records the whole house …
Energy management using non-intrusive load monitoring techniques–State-of-the-art and future research directions
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 …
Non-intrusive load monitoring: A review
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 …
A systematic review of hidden Markov models and their applications
B Mor, S Garhwal, A Kumar - Archives of computational methods in …, 2021 - Springer
The hidden Markov models are statistical models used in many real-world applications and
communities. The use of hidden Markov models has become predominant in the last …
communities. The use of hidden Markov models has become predominant in the last …
Is disaggregation the holy grail of energy efficiency? The case of electricity
This paper aims to address two timely energy problems. First, significant low-cost energy
reductions can be made in the residential and commercial sectors, but these savings have …
reductions can be made in the residential and commercial sectors, but these savings have …
NILMTK: An open source toolkit for non-intrusive load monitoring
Non-intrusive load monitoring, or energy disaggregation, aims to separate household
energy consumption data collected from a single point of measurement into appliance-level …
energy consumption data collected from a single point of measurement into appliance-level …
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 …
the recorded mains in a household. NILM is unidentifiable and thus a challenge problem …