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 …

Smart energy meters for smart grids, an internet of things perspective

YM Rind, MH Raza, M Zubair, MQ Mehmood… - Energies, 2023 - mdpi.com
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 …

A review of distribution network applications based on smart meter data analytics

CL Athanasiadis, TA Papadopoulos… - … and Sustainable Energy …, 2024 - Elsevier
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 …

the Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

S Athanasoulias, F Guasselli, N Doulamis, A Doulamis… - Scientific Data, 2024 - nature.com
The growing availability of smart meter data has facilitated the development of energy-
saving services like demand response, personalized energy feedback, and non-intrusive …

Microgrid energy management and monitoring systems: A comprehensive review

AJ Albarakati, Y Boujoudar, M Azeroual… - Frontiers in Energy …, 2022 - frontiersin.org
Microgrid (MG) technologies offer users attractive characteristics such as enhanced power
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

A Faustine, L Pereira - IEEE Transactions on Smart Grid, 2022 - ieeexplore.ieee.org
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 …

A smart home energy management system utilizing neurocomputing-based time-series load modeling and forecasting facilitated by energy decomposition for smart …

YH Lin, HS Tang, TY Shen, CH Hsia - IEEE Access, 2022 - ieeexplore.ieee.org
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 …

Machine learning applications for smart building energy utilization: a survey

M Huotari, A Malhi, K Främling - Archives of Computational Methods in …, 2024 - Springer
Abstract The United Nations launched sustainable development goals in 2015 that include
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

R Yuan, SA Pourmousavi, WL Soong, AJ Black… - Cell Reports Physical …, 2024 - cell.com
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 …

A residential labeled dataset for smart meter data analytics

L Pereira, D Costa, M Ribeiro - Scientific Data, 2022 - nature.com
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 …