Ensemble reinforcement learning: A survey
Reinforcement Learning (RL) has emerged as a highly effective technique for addressing
various scientific and applied problems. Despite its success, certain complex tasks remain …
various scientific and applied problems. Despite its success, certain complex tasks remain …
Deep Neural Networks in Power Systems: A Review
M Khodayar, J Regan - Energies, 2023 - mdpi.com
Identifying statistical trends for a wide range of practical power system applications,
including sustainable energy forecasting, demand response, energy decomposition, and …
including sustainable energy forecasting, demand response, energy decomposition, and …
RDERL: Reliable deep ensemble reinforcement learning-based recommender system
Recommender systems (RSs) have been employed for many real-world applications
including search engines, social networks, and information retrieval systems as powerful …
including search engines, social networks, and information retrieval systems as powerful …
Forecasting hourly day-ahead solar photovoltaic power generation by assembling a new adaptive multivariate data analysis with a long short-term memory network
Accurate multi-step PV power forecasting is a challenging task because of complex time
series and error buildup in muti-step forecasts. This work is based on developing a …
series and error buildup in muti-step forecasts. This work is based on developing a …
A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies
V Simankov, P Buchatskiy, A Kazak, S Teploukhov… - Energies, 2024 - mdpi.com
The use of renewable energy sources is becoming increasingly widespread around the
world due to various factors, the most relevant of which is the high environmental …
world due to various factors, the most relevant of which is the high environmental …
[HTML][HTML] Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system
The need to reduce global carbon emissions has led to a significant increase in clean
energy globally. While renewable energy penetration into energy grids and power systems …
energy globally. While renewable energy penetration into energy grids and power systems …
Investigating the energy production through sustainable sources by incorporating multifarious machine learning methodologies
Artificial Intelligence (AI) has the potential to revolutionize the way we predict and manage
energy generation from solar and wind sources. It can greatly enhance the accuracy and …
energy generation from solar and wind sources. It can greatly enhance the accuracy and …
[PDF][PDF] A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies. Energies 2024, 17, 416
V Simankov, P Buchatskiy, A Kazak, S Teploukhov… - 2024 - researchgate.net
The use of renewable energy sources is becoming increasingly widespread around the
world due to various factors, the most relevant of which is the high environmental …
world due to various factors, the most relevant of which is the high environmental …
Very short-term solar ultraviolet-A radiation forecasting system with cloud cover images and a Bayesian optimized interpretable artificial intelligence model
High-dose single exposures of long-wavelength ultraviolet-A (UV-A) radiation may trigger
severe biological and skin tissue damage in humans and animals, as well as photosynthetic …
severe biological and skin tissue damage in humans and animals, as well as photosynthetic …
Spatiotemporal Deep Learning for Power System Applications: A Survey
M Saffari, M Khodayar - IEEE Access, 2024 - ieeexplore.ieee.org
Understanding spatiotemporal correlations in power systems is crucial for maintaining grid
stability, reliability, and efficiency. By discerning connections between spatial and temporal …
stability, reliability, and efficiency. By discerning connections between spatial and temporal …