Artificial intelligence techniques in smart grid: A survey
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 …
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
Smartgrid is a paradigm that was introduced into the conventional electricity network to
enhance the way generation, transmission, and distribution networks interrelate. It involves …
enhance the way generation, transmission, and distribution networks interrelate. It involves …
Load forecasting techniques and their applications in smart grids
The growing success of smart grids (SGs) is driving increased interest in load forecasting
(LF) as accurate predictions of energy demand are crucial for ensuring the reliability …
(LF) as accurate predictions of energy demand are crucial for ensuring the reliability …
Gated recurrent unit network-based short-term photovoltaic forecasting
Y Wang, W Liao, Y Chang - Energies, 2018 - mdpi.com
Photovoltaic power has great volatility and intermittency due to environmental factors.
Forecasting photovoltaic power is of great significance to ensure the safe and economical …
Forecasting photovoltaic power is of great significance to ensure the safe and economical …
Deep learning models for long-term solar radiation forecasting considering microgrid installation: A comparative study
Microgrid is becoming an essential part of the power grid regarding reliability, economy, and
environment. Renewable energies are main sources of energy in microgrids. Long-term …
environment. Renewable energies are main sources of energy in microgrids. Long-term …
An adaptive backpropagation algorithm for long-term electricity load forecasting
NA Mohammed, A Al-Bazi - Neural Computing and Applications, 2022 - Springer
Abstract Artificial Neural Networks (ANNs) have been widely used to determine future
demand for power in the short, medium, and long terms. However, research has identified …
demand for power in the short, medium, and long terms. However, research has identified …
A novel sequence to sequence data modelling based CNN-LSTM algorithm for three years ahead monthly peak load forecasting
Long-term load forecasting (LTLF) models play an important role in the strategic planning of
power systems around the globe. Obtaining correct decisions on power network expansions …
power systems around the globe. Obtaining correct decisions on power network expansions …
[HTML][HTML] An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production
G Etxegarai, A López, N Aginako… - Energy for Sustainable …, 2022 - Elsevier
Renewable energies are the alternative that leads to a cleaner generation and a reduction
in CO 2 emissions. However, their dependency on weather makes them unreliable …
in CO 2 emissions. However, their dependency on weather makes them unreliable …
Two-layer optimal scheduling of integrated electric-hydrogen energy system with seasonal energy storage
Hydrogen is characterized by zero carbon emissions and high energy density, which can
effectively support the consumption of a high proportion of intermittent new energy …
effectively support the consumption of a high proportion of intermittent new energy …
Building trend fuzzy granulation-based LSTM recurrent neural network for long-term time-series forecasting
Y Tang, F Yu, W Pedrycz, X Yang… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
The existing long-term time-series forecasting methods based on the neural networks suffer
from multiple limitations, such as accumulated errors and diminishing temporal correlation …
from multiple limitations, such as accumulated errors and diminishing temporal correlation …