A novel forecast scenario-based robust energy management method for integrated rural energy systems with greenhouses
H Tan, Z Li, Q Wang, MA Mohamed - Applied Energy, 2023 - Elsevier
Traditional energy supplies in rural areas are mainly rural power grids and fossil energy,
which makes energy use inefficient and is not environmentally unfriendly. With the …
which makes energy use inefficient and is not environmentally unfriendly. With the …
[HTML][HTML] A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests
Regional forecasting is crucial for a balanced energy delivery system and for achieving the
global transition to clean energy. However, regional wind forecasting is challenging due to …
global transition to clean energy. However, regional wind forecasting is challenging due to …
Short-term wind power scenario generation based on conditional latent diffusion models
X Dong, Z Mao, Y Sun, X Xu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Quantifying short-term uncertainty in wind power plays a crucial role in power system
decision-making. In recent years, the scenario generation community has conducted …
decision-making. In recent years, the scenario generation community has conducted …
Probabilistic online learning framework for short-term wind power forecasting using ensemble bagging regression model
The increasing penetration of renewable energy sources, with a notable focus on wind
power, within modern electricity grids requires computationally efficient and burden-free …
power, within modern electricity grids requires computationally efficient and burden-free …
Geometric loss-enabled complex neural network for multi-energy load forecasting in integrated energy systems
Accurate multi-energy load forecasting plays an important role in the stable and secure
operation of integrated energy systems (IESs). The strong randomness and complex …
operation of integrated energy systems (IESs). The strong randomness and complex …
Short-term wind power forecast based on continuous conditional random field
The randomness and volatility of wind power severely challenge the safety and economy of
power grids. Most short-term forecasting models exclusively concentrate on the correlation …
power grids. Most short-term forecasting models exclusively concentrate on the correlation …
Day-ahead parametric probabilistic forecasting of wind and solar power generation using bounded probability distributions and hybrid neural networks
T Konstantinou, N Hatziargyriou - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The penetration of renewable energy sources in modern power systems increases at an
impressive rate. Due to their intermittent and uncertain nature, it is important to forecast their …
impressive rate. Due to their intermittent and uncertain nature, it is important to forecast their …
Nonparametric Stochastic Differential Equations for Ultra-Short-Term Probabilistic Forecasting of Wind Power Generation
Ultra-short-term probabilistic wind power forecasting provides paramount uncertainty
information for power system real-time operation. However, the stochastic dynamics of wind …
information for power system real-time operation. However, the stochastic dynamics of wind …
Probabilistic Multienergy Load Forecasting Based on Hybrid Attention-Enabled Transformer Network and Gaussian Process-Aided Residual Learning
Precise multienergy load forecasting (MELF) significantly contributes to the stable and
economic operation of integrated energy systems (IES). However, existing MELF …
economic operation of integrated energy systems (IES). However, existing MELF …
Continuous and distribution-free probabilistic wind power forecasting: A conditional normalizing flow approach
We present a data-driven approach for probabilistic wind power forecasting based on
conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution …
conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution …