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Prospects and challenges of the machine learning and data-driven methods for the predictive analysis of power systems: A review
W Strielkowski, A Vlasov, K Selivanov, K Muraviev… - Energies, 2023 - mdpi.com
The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …
systems offers the potential to accurately predict and manage the behavior of these systems …
Smart energy meters for smart grids, an internet of things perspective
Smart energy has evolved over the years to include multiple domains integrated across
multiple technology themes, such as electricity, smart grid, and logistics, linked through …
multiple technology themes, such as electricity, smart grid, and logistics, linked through …
A review of distribution network applications based on smart meter data analytics
The large-scale roll-out of smart meters allows the collection of a vast amount of fine-grained
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …
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 …
Microgrid energy management and monitoring systems: A comprehensive review
Microgrid (MG) technologies offer users attractive characteristics such as enhanced power
quality, stability, sustainability, and environmentally friendly energy through a control and …
quality, stability, sustainability, and environmentally friendly energy through a control and …
FPSeq2Q: Fully parameterized sequence to quantile regression for net-load forecasting with uncertainty estimates
The increased penetration of Renewable Energy Sources (RES) as part of a decentralized
and distributed power system makes net-load forecasting a critical component in the …
and distributed power system makes net-load forecasting a critical component in the …
A smart home energy management system utilizing neurocomputing-based time-series load modeling and forecasting facilitated by energy decomposition for smart …
The key advantage of using power-utility-owned smart meters is the ability to transmit
electrical energy consumption data to power utilities' remote data centers for various …
electrical energy consumption data to power utilities' remote data centers for various …
Machine learning applications for smart building energy utilization: a survey
Abstract The United Nations launched sustainable development goals in 2015 that include
goals for sustainable energy. From global energy consumption, households consume 20 …
goals for sustainable energy. From global energy consumption, households consume 20 …
Unleashing the benefits of smart grids by overcoming the challenges associated with low-resolution data
Smart meters have been widely deployed worldwide, but there is an often-overlooked
problem that remains unresolved: the data collected from these meters is of relatively low …
problem that remains unresolved: the data collected from these meters is of relatively low …
A residential labeled dataset for smart meter data analytics
Smart meter data is a cornerstone for the realization of next-generation electrical power
grids by enabling the creation of novel energy data-based services like providing …
grids by enabling the creation of novel energy data-based services like providing …