Deep learning-based forecasting approach in smart grids with microclustering and bidirectional LSTM network
Uncertainty modeling of renewable energy sources, load demand, electricity price, etc.
create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting …
create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting …
A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid
HJ Kim, MK Kim - Applied Energy, 2023 - Elsevier
Recently, the integration of renewable energy sources (RESs) in microgrids (MGs) has risen
significantly owing to extensive promotion of decarbonization and green energy. However …
significantly owing to extensive promotion of decarbonization and green energy. However …
[HTML][HTML] A multi-step time-series clustering-based Seq2Seq LSTM learning for a single household electricity load forecasting
Z Masood, R Gantassi, Y Choi - Energies, 2022 - mdpi.com
The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting
the energy industry into a modern era of reliable and sustainable energy networks. This …
the energy industry into a modern era of reliable and sustainable energy networks. This …
[HTML][HTML] Deep learning-assisted short-term load forecasting for sustainable management of energy in microgrid
Nowadays, supplying demand load and maintaining sustainable energy are important
issues that have created many challenges in power systems. In these types of problems …
issues that have created many challenges in power systems. In these types of problems …
A performance comparison of machine learning algorithms for load forecasting in smart grid
With the rapid increase in the world's population, the global electricity demand has
increased drastically. Therefore, it is required to adopt efficient energy management …
increased drastically. Therefore, it is required to adopt efficient energy management …
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Electric load forecasting is essential for the planning and maintenance of power systems.
However, its un-stationary and non-linear properties impose significant difficulties in …
However, its un-stationary and non-linear properties impose significant difficulties in …
Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid
G Hafeez, KS Alimgeer, I Khan - Applied Energy, 2020 - Elsevier
Accurate electric load forecasting is important due to its application in the decision making
and operation of the power grid. However, the electric load profile is a complex signal due to …
and operation of the power grid. However, the electric load profile is a complex signal due to …
[HTML][HTML] Deep long short-term memory: A new price and load forecasting scheme for big data in smart cities
This paper focuses on analytics of an extremely large dataset of smart grid electricity price
and load, which is difficult to process with conventional computational models. These data …
and load, which is difficult to process with conventional computational models. These data …
A novel hybrid short-term load forecasting method of smart grid using MLR and LSTM neural network
The short-term load forecasting is crucial in the power system operation and control.
However, due to its nonstationary and complicated random features, an accurate forecast of …
However, due to its nonstationary and complicated random features, an accurate forecast of …
[HTML][HTML] Accurate deep model for electricity consumption forecasting using multi-channel and multi-scale feature fusion CNN–LSTM
X Shao, CS Kim, P Sontakke - Energies, 2020 - mdpi.com
Electricity consumption forecasting is a vital task for smart grid building regarding the supply
and demand of electric power. Many pieces of research focused on the factors of weather …
and demand of electric power. Many pieces of research focused on the factors of weather …