Daily power demand prediction for buildings at a large scale using a hybrid of physics-based model and generative adversarial network

Keyphrases

• Physics-based Model 100%
• Generative Adversarial Networks 100%
• Power Demand 55%
• Data-driven Modeling 33%
• EnergyPlus 22%
• Computing Time 22%
• Mean Absolute Percentage Error 22%
• Popular 11%
• Prediction Method 11%
• Prediction Error 11%
• Prediction Accuracy 11%
• Heavy Workload 11%
• Polynomial Regression Model 11%
• Network Method 11%
• Residential Buildings 11%
• Top-down Method 11%
• Power Consumption Pattern 11%
• Building Category 11%
• Extreme Learning Machine Regression 11%
• Model Creation 11%
• Single Building 11%
• Support Vector Regression 11%

Engineering

• Energy Demand 100%
• Generative Adversarial Networks 100%
• EnergyPlus 22%
• Computing Time 22%
• Limitations 11%
• Prediction Error 11%
• Similarities 11%
• Power Grid 11%
• Residential Building 11%
• Support Vector Machine 11%
• Extreme Learning Machine 11%