A Data-Driven Approach for Long-Term Building Energy Demand Prediction

Keyphrases

• Data-driven Approach 100%
• Building Energy Consumption 100%
• Energy Consumption Prediction 100%
• Neighborhood Features 100%
• Energy Disclosure 100%
• Coefficient of Variation 50%
• Performance Results 50%
• Prediction Method 50%
• Knowledge Gaps 50%
• Prediction Model 50%
• Temporal Variation 50%
• Urban Areas 50%
• Physical Features 50%
• Energy Consumption Reduction 50%
• Strategy Choice 50%
• New York City 50%
• Commercial Buildings 50%
• Policy Decisions 50%
• Residential Buildings 50%
• Energy Consumption 50%
• Carbon Dioxide Emissions 50%
• Population Growth 50%
• Built Environment 50%
• Building Energy Performance 50%
• Rapid Urbanization 50%
• Large Volumes of Data 50%
• Number of Cities 50%
• Local Officials 50%
• Energy Assessment 50%
• Disclosure Policy 50%
• Sustainable World 50%
• Energy Consumption Data 50%
• Supply Policy 50%
• Ensemble Machine Learning 50%
• Energy Issues 50%
• Actual Energy Consumption 50%
• Long-term Energy Demand 50%
• Energy Supply Strategy 50%

Engineering

• Energy Engineering 100%
• Coefficient of Variation 25%
• Learning Algorithm 25%
• Temporal Variation 25%
• Commercial Building 25%
• Residential Building 25%
• Built Environment 25%
• Term Energy 25%
• Building Energy Performance 25%
• Reduce Energy Consumption 25%
• Policy Decision 25%
• Energy Consumption Data 25%
• Energy Issue 25%