A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an …
Accurate wind speed forecasting plays an essential role in scheduling wind power
generation. Currently, most existing models predict wind speed just based on temporal …
generation. Currently, most existing models predict wind speed just based on temporal …
Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables
Accurate wind speed forecasting plays a crucial role in the efficient and economical
management of power supply systems. In this study, a novel framework combining …
management of power supply systems. In this study, a novel framework combining …
High and low frequency wind power prediction based on Transformer and BiGRU-Attention
S Wang, J Shi, W Yang, Q Yin - Energy, 2024 - Elsevier
An accurate and reliable wind power prediction model has important significance for the
operation of power systems and large-scale grid connection. This paper proposes a hybrid …
operation of power systems and large-scale grid connection. This paper proposes a hybrid …
Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China
The spatio-temporal wind speed prediction technology provides the key technical support for
the energy management and space allocation of the wind farm. To obtain an accurate spatio …
the energy management and space allocation of the wind farm. To obtain an accurate spatio …
A novel hybrid deep learning model for multi-step wind speed forecasting considering pairwise dependencies among multiple atmospheric variables
The reliable wind speed forecasting is critical for wind farms as it enables them to improve
cost-effectiveness and optimize energy efficiency. In this study, a novel hybrid deep learning …
cost-effectiveness and optimize energy efficiency. In this study, a novel hybrid deep learning …
Spatio-temporal interpretable neural network for solar irradiation prediction using transformer
Deep learning models have been increasingly applied in the field of solar radiation
prediction. However, the characteristics of a deep learning black box model restrict its …
prediction. However, the characteristics of a deep learning black box model restrict its …
GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations
N Zhu, Y Wang, K Yuan, J Yan, Y Li, K Zhang - Applied Energy, 2024 - Elsevier
Power forecast for each renewable power plant (RPP) in the renewable energy clusters is
essential. Though existing graph neural networks (GNN)-based models achieve satisfactory …
essential. Though existing graph neural networks (GNN)-based models achieve satisfactory …
Mixformer: Mixture transformer with hierarchical context for spatio-temporal wind speed forecasting
Wind energy has attracted more and more attention due to its sustainability and pollution-
free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of …
free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of …
FECAM: Frequency enhanced channel attention mechanism for time series forecasting
Time series forecasting (TSF) is a challenging problem in various real-world scenarios, such
as industry, energy, weather, traffic, economics, and earthquake warning. TSF demands the …
as industry, energy, weather, traffic, economics, and earthquake warning. TSF demands the …
The Potential of Machine Learning for Wind Speed and Direction Short-Term Forecasting: A Systematic Review
Wind forecasting, which is essential for numerous services and safety, has significantly
improved in accuracy due to machine learning advancements. This study reviews 23 articles …
improved in accuracy due to machine learning advancements. This study reviews 23 articles …