Scale-and context-aware convolutional non-intrusive load monitoring
Non-intrusive load monitoring addresses the challenging task of decomposing the
aggregate signal of a household's electricity consumption into appliance-level data without …
aggregate signal of a household's electricity consumption into appliance-level data without …
[HTML][HTML] Leveraging sequence-to-sequence learning for online non-intrusive load monitoring in edge device
Non-intrusive load monitoring (NILM), extracting the appliances' usage profiles by
decomposing a household's aggregate electricity consumption, has become increasingly …
decomposing a household's aggregate electricity consumption, has become increasingly …
Efficient model-based deep reinforcement learning with variational state tabulation
Modern reinforcement learning algorithms reach super-human performance on many board
and video games, but they are sample inefficient, ie they typically require significantly more …
and video games, but they are sample inefficient, ie they typically require significantly more …
Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling
Nonintrusive load monitoring (NILM) is an important technique for energy management and
conservation. In this paper, a deep learning model based on an attention mechanism …
conservation. In this paper, a deep learning model based on an attention mechanism …
Neural variational identification and filtering for stochastic non-linear dynamical systems with application to non-intrusive load monitoring
In this paper, an algorithm for performing System Identification and inference of the filtering
recursion for stochastic non-linear dynamical systems is introduced. Additionally, the …
recursion for stochastic non-linear dynamical systems is introduced. Additionally, the …
Variational bolt: Approximate learning in factorial hidden markov models with application to energy disaggregation
H Lange, M Bergés - Proceedings of the AAAI Conference on Artificial …, 2018 - ojs.aaai.org
The learning problem for Factorial Hidden Markov Models with discrete and multi-variate
latent variables remains a challenge. Inference of the latent variables required for the E-step …
latent variables remains a challenge. Inference of the latent variables required for the E-step …
A dynamic edge exchangeable model for sparse temporal networks
YC Ng, R Silva - arXiv preprint arXiv:1710.04008, 2017 - arxiv.org
We propose a dynamic edge exchangeable network model that can capture sparse
connections observed in real temporal networks, in contrast to existing models which are …
connections observed in real temporal networks, in contrast to existing models which are …
Data-Driven operation of building systems: present challenges and future prospects
In this paper we review the current landscape of data-driven decision making in the context
of operating residential and commercial building systems with energy management …
of operating residential and commercial building systems with energy management …
Towards applicability: A comparative study on non-intrusive load monitoring algorithms
Non-intrusive load monitoring is an important energy disaggregation technology, which can
provide appliance-level consumption estimation given a series of total consumption over …
provide appliance-level consumption estimation given a series of total consumption over …
Sequence-to-point learning methods for Non Intrusive Load Disaggregation: A Review
TT Kuzengurira, W Guangfen… - 2024 6th International …, 2024 - ieeexplore.ieee.org
Energy disaggregation, also known as non-intrusive load monitoring (NILM), is a single-
channel blind source separation (BSS) problem. It aims to decompose the total energy …
channel blind source separation (BSS) problem. It aims to decompose the total energy …