Review on interpretable machine learning in smart grid
In recent years, machine learning, especially deep learning, has developed rapidly and has
shown remarkable performance in many tasks of the smart grid field. The representation …
shown remarkable performance in many tasks of the smart grid field. The representation …
Smart grid security: Attacks and defence techniques
The smart grid (SG) consists of three main components, that is, Information Technology (IT),
Operational Technology (OT), and Advanced Metring Infrastructure (AMI). Due to the …
Operational Technology (OT), and Advanced Metring Infrastructure (AMI). Due to the …
[HTML][HTML] Multivariate time series prediction by RNN architectures for energy consumption forecasting
Households and buildings have been utilizing the traditional electric network structure for
the last decade, relying on energy supplied by manufacturing centers based on fossil fuels …
the last decade, relying on energy supplied by manufacturing centers based on fossil fuels …
Machine learning short-term energy consumption forecasting for microgrids in a manufacturing plant
Energy production and supply are important challenges for civilisation. Renewable energy
sources present an increased share of the energy supply. Under these circumstances, small …
sources present an increased share of the energy supply. Under these circumstances, small …
[HTML][HTML] A VAE-Bayesian deep learning scheme for solar power generation forecasting based on dimensionality reduction
The advancements in distributed generation (DG) technologies such as solar panels have
led to a widespread integration of renewable power generation in modern power systems …
led to a widespread integration of renewable power generation in modern power systems …
The age of correlated features in supervised learning based forecasting
In this paper, we analyze the impact of information freshness on supervised learning based
forecasting. In these applications, a neural network is trained to predict a time-varying target …
forecasting. In these applications, a neural network is trained to predict a time-varying target …
A variational autoencoder-based dimensionality reduction technique for generation forecasting in cyber-physical smart grids
Modern energy systems often regarded as smart grid (SG) systems are cyber-physical
systems (CPS) equipped with advanced metering and smart sensing devices, leading to a …
systems (CPS) equipped with advanced metering and smart sensing devices, leading to a …
Application of the deep CNN-based method in industrial system for wire marking identification
Industry 4.0, a term invented by Wolfgang Wahlster in Germany, is celebrating its 10th
anniversary in 2021. Still, the digitalization of the production environment is one of the …
anniversary in 2021. Still, the digitalization of the production environment is one of the …
A bayesian deep learning technique for multi-step ahead solar generation forecasting
In this paper, we propose an improved Bayesian bidirectional long-short term memory
(BiLSTM) neural networks for multi-step ahead (MSA) solar generation forecasting. The …
(BiLSTM) neural networks for multi-step ahead (MSA) solar generation forecasting. The …
Combating Uncertainties in Smart Grid Decision Networks: Multi-Agent Reinforcement Learning With Imperfect State Information
A Ghasemi, A Shojaeighadikolaei… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Renewable energy sources, such as wind and solar power, are increasingly being
integrated into smart grid systems. However, when compared to traditional energy …
integrated into smart grid systems. However, when compared to traditional energy …