Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the …
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …
autonomous software that optimizes decision-making and energy distribution operations …
A review of deep learning for renewable energy forecasting
As renewable energy becomes increasingly popular in the global electric energy grid,
improving the accuracy of renewable energy forecasting is critical to power system planning …
improving the accuracy of renewable energy forecasting is critical to power system planning …
[HTML][HTML] A review and taxonomy of wind and solar energy forecasting methods based on deep learning
G Alkhayat, R Mehmood - Energy and AI, 2021 - Elsevier
Renewable energy is essential for planet sustainability. Renewable energy output
forecasting has a significant impact on making decisions related to operating and managing …
forecasting has a significant impact on making decisions related to operating and managing …
State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques
Forecasting renewable energy efficiency significantly impacts system management and
operation because more precise forecasts mean reduced risk and improved stability and …
operation because more precise forecasts mean reduced risk and improved stability and …
A review of graph neural networks and their applications in power systems
W Liao, B Bak-Jensen, JR Pillai… - Journal of Modern …, 2021 - ieeexplore.ieee.org
Deep neural networks have revolutionized many machine learning tasks in power systems,
ranging from pattern recognition to signal processing. The data in these tasks are typically …
ranging from pattern recognition to signal processing. The data in these tasks are typically …
Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting
This paper presents a deep residual network for improving time-series forecasting models,
indispensable to reliable and economical power grid operations, especially with high shares …
indispensable to reliable and economical power grid operations, especially with high shares …
Spatio-temporal graph neural networks for multi-site PV power forecasting
J Simeunović, B Schubnel, PJ Alet… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Accurate forecasting of solar power generation with fine temporal and spatial resolution is
vital for the operation of the power grid. However, state-of-the-art approaches that combine …
vital for the operation of the power grid. However, state-of-the-art approaches that combine …
Deep learning in power systems research: A review
With the rapid growth of power systems measurements in terms of size and complexity,
discovering statistical patterns for a large variety of real-world applications such as …
discovering statistical patterns for a large variety of real-world applications such as …
Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model
The stability operation and real-time control of the integrated energy system with distributed
energy resources determines the higher and higher requirements for the accuracy of solar …
energy resources determines the higher and higher requirements for the accuracy of solar …
Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method
F Wang, P Chen, Z Zhen, R Yin, C Cao, Y Zhang… - Applied energy, 2022 - Elsevier
Accurate wind farm cluster power forecasting is of great significance for the safe operation of
the power system with high wind power penetration. However, most of the current neural …
the power system with high wind power penetration. However, most of the current neural …