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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 …
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 …
Review on deep neural networks applied to low-frequency nilm
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 …
A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context
The rising demand for energy conservation in residential buildings has increased interest in
load monitoring techniques by exploiting energy consumption data. In recent years …
load monitoring techniques by exploiting energy consumption data. In recent years …
[HTML][HTML] 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 …
[HTML][HTML] An active learning framework for the low-frequency non-intrusive load monitoring problem
With the widespread deployment of smart meters worldwide, quantification of energy used
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
Deep learning in economics: a systematic and critical review
From the perspective of historical review, the methodology of economics develops from
qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of …
qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of …
the Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece
The growing availability of smart meter data has facilitated the development of energy-
saving services like demand response, personalized energy feedback, and non-intrusive …
saving services like demand response, personalized energy feedback, and non-intrusive …
Electricity: An efficient transformer for non-intrusive load monitoring
Non-Intrusive Load Monitoring (NILM) describes the process of inferring the consumption
pattern of appliances by only having access to the aggregated household signal. Sequence …
pattern of appliances by only having access to the aggregated household signal. Sequence …
Modeling air quality PM2. 5 forecasting using deep sparse attention-based transformer networks
Z Zhang, S Zhang - International journal of environmental science and …, 2023 - Springer
Air quality forecasting is of great importance in environmental protection, government
decision-making, people's daily health, etc. Existing research methods have failed to …
decision-making, people's daily health, etc. Existing research methods have failed to …