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 …
Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges
The integration of large-scale wind power introduces issues in modern power systems
operations due to its strong randomness and volatility. These issues can be resolved via …
operations due to its strong randomness and volatility. These issues can be resolved via …
A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting
Y Wang, H Xu, M Song, F Zhang, Y Li, S Zhou, L Zhang - Applied Energy, 2023 - Elsevier
Wind speed forecasting plays an important role in the stable operation of wind energy power
systems. However, accurate and reliable wind speed forecasting faces four challenges: how …
systems. However, accurate and reliable wind speed forecasting faces four challenges: how …
Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
MHDM Ribeiro, RG da Silva, SR Moreno… - International Journal of …, 2022 - Elsevier
The use of wind energy plays a vital role in society owing to its economic and environmental
importance. Knowing the wind power generation within a specific time window is useful for …
importance. Knowing the wind power generation within a specific time window is useful for …
A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
Wind energy is one of the sources which is still in development in Brazil. However, it already
represents 17% of the National Interconnected System. Due to the high level of uncertainty …
represents 17% of the National Interconnected System. Due to the high level of uncertainty …
Interval forecasting for urban water demand using PSO optimized KDE distribution and LSTM neural networks
B Du, S Huang, J Guo, H Tang, L Wang, S Zhou - Applied Soft Computing, 2022 - Elsevier
The current literature on water demand forecasting mostly focuses on giving accurate point
predictions of water demand. However, the water demand point forecasting will encounter …
predictions of water demand. However, the water demand point forecasting will encounter …
A review on multi-objective optimization framework in wind energy forecasting techniques and applications
Wind energy is renewable and clean energy. To improve the utilization efficiency of the wind
energy, studies on wind energy forecasting have gradually been developed in recent years …
energy, studies on wind energy forecasting have gradually been developed in recent years …
Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization
This study proposes an effective combined model system for wind speed forecasting tasks.
In this model,(a) improved hybrid time series decomposition strategy (HTD) is developed to …
In this model,(a) improved hybrid time series decomposition strategy (HTD) is developed to …
Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
SR Moreno, RG da Silva, VC Mariani… - Energy Conversion and …, 2020 - Elsevier
The intermittent nature of wind can represent an obstacle to get reliable wind speed
forecasting, thus many methods were developed to improve the accuracy, due to unstable …
forecasting, thus many methods were developed to improve the accuracy, due to unstable …
Electrical load forecasting: A deep learning approach based on K-nearest neighbors
Y Dong, X Ma, T Fu - Applied Soft Computing, 2021 - Elsevier
Deep learning approaches have shown superior advantages than shallow techniques in the
field of electrical load forecasting; however, their applications in existing studies encounter …
field of electrical load forecasting; however, their applications in existing studies encounter …