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 …
Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning
Accurate output forecasts are essential for photovoltaic projects to achieve stable power
supply. Traditional forecasts based on ground observation time series are widely troubled by …
supply. Traditional forecasts based on ground observation time series are widely troubled by …
[HTML][HTML] Advances in solar forecasting: Computer vision with deep learning
Renewable energy forecasting is crucial for integrating variable energy sources into the grid.
It allows power systems to address the intermittency of the energy supply at different …
It allows power systems to address the intermittency of the energy supply at different …
Review on the application of photovoltaic forecasting using machine learning for very short-to long-term forecasting
PNL Mohamad Radzi, MN Akhter, S Mekhilef… - Sustainability, 2023 - mdpi.com
Advancements in renewable energy technology have significantly reduced the consumer
dependence on conventional energy sources for power generation. Solar energy has …
dependence on conventional energy sources for power generation. Solar energy has …
Short-term solar power prediction learning directly from satellite images with regions of interest
Developing solar power generation technology is an efficient approach to relieving the
global environmental crisis. However, solar energy is an energy source with strong …
global environmental crisis. However, solar energy is an energy source with strong …
Application of machine learning to evaluating and remediating models for energy and environmental engineering
Abstract Machine learning (ML) algorithms have been increasingly successful in their
applications to solve energy and environmental engineering problems. ML algorithms have …
applications to solve energy and environmental engineering problems. ML algorithms have …
Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy
L Yin, Y Wu - Applied Energy, 2022 - Elsevier
The large-scale application of renewable energy can promote the global goal of carbon
neutrality. However, the stochastic nature of wind and solar energy aggravates the active …
neutrality. However, the stochastic nature of wind and solar energy aggravates the active …
Lowest-threshold solar laser operation under cloudy sky condition
Classical solar-pumped lasers often demand a significant amount of concentrated solar
power for laser emission, which is only attainable under clear sky condition, limiting their …
power for laser emission, which is only attainable under clear sky condition, limiting their …
Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm
VT Aghaei, A Ağababaoğlu, B Bawo… - Applied Energy, 2023 - Elsevier
This study focuses on the numerical analysis and optimal control of vertical-axis wind
turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically address small …
turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically address small …
Deep learning for intra-hour solar forecasting with fusion of features extracted from infrared sky images
G Terrén-Serrano, M Martínez-Ramón - Information Fusion, 2023 - Elsevier
The increasing penetration of solar energy leaves power grids vulnerable to fluctuations in
the solar radiation that reaches the surface of the Earth due to the projection of cloud …
the solar radiation that reaches the surface of the Earth due to the projection of cloud …