A spatiotemporal wind power prediction based on wavelet decomposition, feature selection, and localized prediction

Keyphrases

• Prediction Method 100%
• Spatiotemporal 100%
• Feature Selection 100%
• Wavelet Decomposition 100%
• Wind Power Prediction 100%
• Decomposition Characteristics 100%
• Canada 16%
• Nonlinearity 16%
• Computation Time 16%
• Decomposition Method 16%
• Performance Evaluation 16%
• Historical Data 16%
• Wind Power Generation 16%
• Wind Power 16%
• Evaluation Index 16%
• Discrete Wavelet Transform 16%
• Non-stationarity 16%
• Primary Barrier 16%
• Wind Farm 16%
• Prediction Value 16%
• Multi-scale Wavelet Decomposition 16%
• Wind Power Time Series 16%
• Double Input 16%
• Saskatchewan 16%
• Selection Stages 16%
• Prediction Engine 16%
• Power Prediction Model 16%
• Spatio-temporal Relations 16%
• High-precision Prediction 16%

Computer Science

• Feature Selection 100%
• Wavelet Decomposition 100%
• Feature Extraction 100%
• Prediction Model 50%
• Historical Data 50%
• Performance Evaluation 50%
• Prediction Accuracy 50%
• Wavelet Transforms 50%
• Evaluation Index 50%
• Prediction Value 50%
• Energy Generation 50%
• Computation Time 50%
• Predicted Value 50%

Engineering

• Feature Extraction 100%
• Wind Power 100%
• Wavelet Decomposition 100%
• Computation Time 12%
• Predicted Value 12%
• Superposition 12%
• Wind Power Generation 12%
• Historical Data 12%
• Stationarity 12%
• Nonlinearity 12%
• Compressed Air Motors 12%