A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

Keyphrases

• Hybrid Model 100%
• Deep Learning Methods 100%
• Evolutionary Model 100%
• Offshore Wind Farm 100%
• Lillgrund 100%
• Short-term Wind Speed Forecasting 100%
• Evolutionary 50%
• Optimization Algorithm 50%
• Hybrid Method 50%
• Decomposition Method 50%
• Challenging Tasks 50%
• Novel Hybrids 50%
• Forecast Horizon 50%
• Machine Learning Models 50%
• Generalized Normal Distribution Optimization 50%
• Baltic Sea 50%
• Applied Machine Learning 50%
• Wind Speed 50%
• Large-scale Integration 50%
• Offshore Wind Turbine 50%
• Evolutionary Approach 50%
• Wind Power Integration 50%
• Hyperparameter Tuning 50%
• Bidirectional Long Short-term Memory (BiLSTM) 50%
• Power Grid 50%
• Wind Power Generation 50%
• Wind Power Technology 50%
• Global Environmental Issues 50%
• Wind Farm 50%
• Wind Speed Forecasting 50%
• Seasonal Characteristics 50%
• Energy Demand 50%
• Hybrid Deep Learning 50%
• Stochastic Characteristics 50%
• Performance Criteria 50%

Engineering

• Offshore Wind Farms 100%
• Deep Learning 100%
• Hybrid Model 100%
• Tasks 50%
• Experimental Result 50%
• Power Engineering 50%
• Normal Distribution 50%
• Wind Power 50%
• Offshore Wind Turbines 50%
• Power Grid 50%
• Long Short-Term Memory 50%
• Hybrid Approach 50%
• Wind Turbine 50%
• Wind Power Generation 50%
• LSI Circuits 50%
• Performance Criterion 50%

Chemical Engineering

• Deep Learning 100%
• Neural Network 50%
• Learning System 50%
• Long Short-Term Memory 50%