Artificial intelligence techniques in smart grid: A survey

OA Omitaomu, H Niu - Smart Cities, 2021 - mdpi.com
The smart grid is enabling the collection of massive amounts of high-dimensional and multi-
type data about the electric power grid operations, by integrating advanced metering …

Integrating artificial intelligence Internet of Things and 5G for next-generation smartgrid: A survey of trends challenges and prospect

E Esenogho, K Djouani, AM Kurien - Ieee Access, 2022 - ieeexplore.ieee.org
Smartgrid is a paradigm that was introduced into the conventional electricity network to
enhance the way generation, transmission, and distribution networks interrelate. It involves …

Distributed load forecasting using smart meter data: Federated learning with Recurrent Neural Networks

MN Fekri, K Grolinger, S Mir - International Journal of Electrical Power & …, 2022 - Elsevier
Load forecasting is essential for energy management, infrastructure planning, grid
operation, and budgeting. Large scale smart meter deployments have resulted in ability to …

A hybrid prediction method for realistic network traffic with temporal convolutional network and LSTM

J Bi, X Zhang, H Yuan, J Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Accurate and real-time prediction of network traffic can not only help system operators
allocate resources rationally according to their actual business needs but also help them …

AI-empowered methods for smart energy consumption: A review of load forecasting, anomaly detection and demand response

X Wang, H Wang, B Bhandari, L Cheng - International Journal of Precision …, 2024 - Springer
This comprehensive review paper aims to provide an in-depth analysis of the most recent
developments in the applications of artificial intelligence (AI) techniques, with an emphasis …

K-means and alternative clustering methods in modern power systems

SM Miraftabzadeh, CG Colombo, M Longo… - Ieee …, 2023 - ieeexplore.ieee.org
As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …

[HTML][HTML] A review of the use of artificial intelligence methods in infrastructure systems

L McMillan, L Varga - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the
growth of digitalisation and has the potential to enable the 'system of systems' approach …

A review of deep learning with special emphasis on architectures, applications and recent trends

S Sengupta, S Basak, P Saikia, S Paul… - Knowledge-Based …, 2020 - Elsevier
Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning

M Tan, S Yuan, S Li, Y Su, H Li… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Power demand forecasting with high accuracy is a guarantee to keep the balance between
power supply and demand. Due to strong volatility of industrial power load, ultra-short-term …

Convolutional and recurrent neural network based model for short-term load forecasting

H Eskandari, M Imani, MP Moghaddam - Electric Power Systems Research, 2021 - Elsevier
The consumed electrical load is affected by many external factors such as weather, season
of the year, weekday or weekend and holiday. In this paper, it is tried to provide a high …