An Ensemble Learning Approach for Energy Demand Forecasting in Microgrids Using Fog Computing

Keyphrases

• Simulation Environment 100%
• Microgrid 100%
• Fog Computing 100%
• Ensemble Learning Approach 100%
• Energy Demand Forecasting 100%
• Fog-based 66%
• Fog Nodes 66%
• Information Exchange 33%
• Energy Demand 33%
• Model Complexity 33%
• Forecasting Model 33%
• Application-oriented 33%
• Low Latency 33%
• Electrical Load 33%
• Smart Grid 33%
• Execution Time 33%
• Least Squares Support Vector Regression (LSSVR) 33%
• Forecasting Applications 33%
• Short-term Load Forecasting 33%
• Ensemble Model 33%
• Long-Short 33%
• Microgrid System 33%
• Smart Meter 33%
• Edge Layer 33%
• Edge Cloud 33%
• Smart Grid System 33%
• Fog Layer 33%
• Energy Demand Management 33%
• Latency Time 33%
• 3-tier Architecture 33%
• Cloud Layer 33%
• Docker 33%
• Time Metrics 33%
• Demand Management System 33%
• Latency-aware 33%
• Raspberry Pi Microcontroller 33%
• Privacy-preserving 33%
• Utility Provider 33%

Computer Science

• Learning Approach 100%
• Simulation Environment 100%
• Fog Computing 100%
• Ensemble Learning 100%
• Long Short-Term Memory Network 33%
• Execution Time 33%
• Support Vector Regression 33%
• Privacy Preserving 33%
• Raspberry Pi 33%
• Smart Meters 33%
• Tier Architecture 33%
• Smart Grid System 33%
• Demand Management System 33%
• Electrical Load 33%
• Smart Grid 33%