Short-term load forecasting based on CEEMDAN and dendritic deep learning

Keyphrases

• Deep Learning 100%
• Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) 100%
• Short-term Load Forecasting 100%
• Dendritic Neuron Model 50%
• Load Data 50%
• Prediction Accuracy 25%
• Humidity 25%
• Learning Model 25%
• Temporal Features 25%
• State-of-the-art Models 25%
• Panama 25%
• Subsequence 25%
• Result Prediction 25%
• Transformer 25%
• Forecasting Techniques 25%
• Prediction Value 25%
• Long Short-term Memory 25%
• Time Series Data 25%
• Volatility 25%
• Generation Resources 25%
• Electricity Load 25%
• Structural Innovation 25%
• Intrinsic Correlation 25%
• Load Uncertainty 25%
• Power Supply Reliability 25%
• Real-time Market 25%
• Operational Efficiency 25%
• Fully Connected Layer 25%

Engineering

• Empirical-Mode Decomposition 100%
• Load Forecasting 100%
• Deep Learning Method 100%
• Long Short-Term Memory 25%
• Data Series 25%
• Influencing Factor 25%
• Subsequence 25%
• Generation Resource 25%
• Load Electricity 25%
• Operational Efficiency 25%