A comprehensive multivariate wind speed forecasting model utilizing deep learning neural networks
D Wei, Z Tian - Arabian Journal for Science and Engineering, 2024 - Springer
Predicting wind speed accurately is essential for the efficient generation of wind power. To
enhance the precision of wind speed forecasting, this paper proposes a multivariate …
enhance the precision of wind speed forecasting, this paper proposes a multivariate …
A novel dynamic ensemble of Numerical Weather Prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling
Accurate wind forecasts for one day ahead or longer periods have significant impacts on the
safe and efficient dispatch of power grids, where Numerical Weather Prediction (NWP) …
safe and efficient dispatch of power grids, where Numerical Weather Prediction (NWP) …
Predicting cobalt ion concentration in hydrometallurgy zinc process using data decomposition and machine learning
Y Tan, W Xu, K Yang, S Pasha, H Wang… - Science of The Total …, 2025 - Elsevier
Solid waste is one of the primary contributors to environmental pollution currently, it is crucial
to enhance the prevention and control of solid waste pollution in environmental …
to enhance the prevention and control of solid waste pollution in environmental …
A two-stage deep learning-based hybrid model for daily wind speed forecasting
Global adoption of wind energy continues to increase, while improving the efficiency of
turbine settings requires reliable wind speed (WS) models. The latest models rely on …
turbine settings requires reliable wind speed (WS) models. The latest models rely on …
[HTML][HTML] Innovative multi-temporal evapotranspiration forecasting using empirical fourier decomposition and bidirectional long short-term memory
Reference evapotranspiration (ET o) is an essential environmental variable that is intimately
significant to agriculture. Managing water and crop planning relies heavily on precise …
significant to agriculture. Managing water and crop planning relies heavily on precise …
[HTML][HTML] Wind power forecasting with metaheuristic-based feature selection and neural networks
Accurate forecasting of wind power generation is crucial for ensuring a stable and efficient
energy supply, reducing the environmental impact of energy production, and promoting a …
energy supply, reducing the environmental impact of energy production, and promoting a …
Predicting the subcutaneous temperature in cryolipolysis using deep operator networks
S Gao, X Wang, Y Wang, Y Zhao, M Gong - Thermal Science and …, 2024 - Elsevier
Accurate monitoring of subcutaneous temperature is crucial for the safety and efficacy of
cryolipolysis. However, existing measurement and simulation methods often require trade …
cryolipolysis. However, existing measurement and simulation methods often require trade …
A hybrid wind speed forecasting model with two-stage data processing based on adaptive neuro-fuzzy inference systems and deep learning algorithms
Z Tian, D Wei - Earth Science Informatics, 2025 - Springer
Wind speed forecasting is vital for managing wind power generation, a renewable energy
source. To improve prediction accuracy, this study proposes a hybrid model combining WT …
source. To improve prediction accuracy, this study proposes a hybrid model combining WT …
[HTML][HTML] Dynamic Gust Detection and Conditional Sequence Modeling for Ultra-Short-Term Wind Speed Prediction
As the foundation for optimizing wind turbine operations and ensuring energy stability, wind
speed forecasting directly impacts the safe operation of the power grid, the rationality of grid …
speed forecasting directly impacts the safe operation of the power grid, the rationality of grid …
Short-term wind power forecasting through stacked and bi directional LSTM techniques
Background Computational intelligence (CI) based prediction models increase the efficient
and effective utilization of resources for wind prediction. However, the traditional recurrent …
and effective utilization of resources for wind prediction. However, the traditional recurrent …