Jiading Jiang
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View article: An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power
An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power Open
Due to the prevalent “small data”, “seasonal”, and “periodicity” characteristics in China’s renewable energy power generation data, there are certain difficulties in long-term power generation prediction. For this reason, this paper uses t…
View article: Application of an Improved State Feedback Control in the Selective Catalytic Reduction Denitrification Systems of Coal Power Units Under Variable Load Conditions
Application of an Improved State Feedback Control in the Selective Catalytic Reduction Denitrification Systems of Coal Power Units Under Variable Load Conditions Open
Selective catalytic reduction (SCR) flue gas denitrification systems are inherently complex, typically embodying characteristics of non-linearity, significant time delays, and susceptibility to multiple disturbances. In the context of coal…
View article: Enhancing the Tracking Performance of Wind Turbine Blade Pitch Angle Control via Model-Free Adaptive Control Algorithm Utilizing Input-Output Differential
Enhancing the Tracking Performance of Wind Turbine Blade Pitch Angle Control via Model-Free Adaptive Control Algorithm Utilizing Input-Output Differential Open
In the context of wind energy systems, maintaining optimal power output in wind turbines when wind speeds exceed rated values necessitates precise regulation of blade pitch through the pitch control system. However, challenges in accuratel…
View article: GWTSP: A multi-state prediction method for short-term wind turbines based on GAT and GL
GWTSP: A multi-state prediction method for short-term wind turbines based on GAT and GL Open
Accurately predicting the state of wind turbines in wind farms helps to improve productivity, perform preventive maintenance, and reduce operation and maintenance costs. Deep learning can handle complex data and automatic feature extractio…
View article: TS_XGB:Ultra-Short-Term Wind Power Forecasting Method Based on Fusion of Time-Spatial Data and XGBoost Algorithm
TS_XGB:Ultra-Short-Term Wind Power Forecasting Method Based on Fusion of Time-Spatial Data and XGBoost Algorithm Open
Ultra-short-term wind power forecasting is of great significance to the safety, stableness, and optimal allocation of the power system. Aiming at the actual needs of increasing accuracy of wind power forecasting, this paper proposes an int…
View article: Carbon trading price forecasting: based on improved deep learning method
Carbon trading price forecasting: based on improved deep learning method Open
Carbon trading price is the core component of the carbon emission trading market. Accurate carbon trading price prediction is of great significance for forming an effective carbon trading market and achieving the "dual carbon" goal. Based …