Evaluating Machine Learning and Heuristic Optimization Based Surrogates as a Replacement for a Complex Building Simulation Model

Keyphrases

• Simulation Model 100%
• Optimization Approach 100%
• Heuristic Optimization 100%
• Building Simulation 100%
• Complex Buildings 100%
• Machine Heuristic 100%
• Machine Learning Optimization 100%
• Random Forest Algorithm 66%
• Genetic Algorithm 33%
• K-nearest 33%
• Execution Time 33%
• Machine Learning Models 33%
• Expected Behaviour 33%
• Training Process 33%
• Learning Behavior 33%
• Execution Speed 33%
• Energy Transition 33%
• Prediction Skill 33%
• Complex Simulation 33%
• Detailed Simulation 33%
• IDA ICE Simulation 33%
• Intelligent Energy Management 33%

Computer Science

• Simulation Model 100%
• Machine Learning 100%
• Learning System 100%
• Random Decision Forest 50%
• Genetic Algorithm 25%
• Simulation Mode 25%
• Execution Time 25%
• Training Process 25%
• Execution Speed 25%
• k-Nearest Neighbors Algorithm 25%

Engineering

• Simulation Model 100%
• Learning System 100%
• Random Forest 50%
• Genetic Algorithm 25%
• Energy Management System 25%
• Execution Time 25%
• Prediction Capability 25%
• Energy Transition 25%
• Simulation Mode 25%
• Nearest Neighbor 25%