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 …
Energy forecasting: A review and outlook
Forecasting has been an essential part of the power and energy industry. Researchers and
practitioners have contributed thousands of papers on forecasting electricity demand and …
practitioners have contributed thousands of papers on forecasting electricity demand and …
Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game
An operating entity utilizing community-integrated energy systems with a large number of
small-scale distributed energy sources can easily trade with existing distribution markets. To …
small-scale distributed energy sources can easily trade with existing distribution markets. To …
Time-series generative adversarial networks
A good generative model for time-series data should preserve temporal dynamics, in the
sense that new sequences respect the original relationships between variables across time …
sense that new sequences respect the original relationships between variables across time …
Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM
More and more photovoltaic (PV) power generation is incorporated into the grid. However,
the intermittence and fluctuation of solar energy have brought huge challenges to the safe …
the intermittence and fluctuation of solar energy have brought huge challenges to the safe …
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 …
Privacy-preserving spatiotemporal scenario generation of renewable energies: A federated deep generative learning approach
Scenario generation is a fundamental and crucial tool for decision-making in power systems
with high-penetration renewables. Based on big historical data, in this article, a novel …
with high-penetration renewables. Based on big historical data, in this article, a novel …
Uncertainty models for stochastic optimization in renewable energy applications
With the rapid surge of renewable energy integrations into the electrical grid, the main
questions remain; how do we manage and operate optimally these surges of fluctuating …
questions remain; how do we manage and operate optimally these surges of fluctuating …
A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems
The optimal co-planning of the integrated energy system (IES) and machine learning (ML)
application on the multivariable prediction of IES parameters have mostly been carried out …
application on the multivariable prediction of IES parameters have mostly been carried out …
Taxonomy research of artificial intelligence for deterministic solar power forecasting
With the world-wide deployment of solar energy for a sustainable and renewable future, the
stochastic and volatile nature of solar power pose significant challenges to the reliable …
stochastic and volatile nature of solar power pose significant challenges to the reliable …