Energy Efficiency Modeling

AI-powered modeling optimizes building energy use and automates compliance verification

Business impact

• Energy consumption — Lower energy use through optimized design and operational adjustments
• Operational efficiency — Streamlined processes reduce waste and improve system performance
• Design accuracy — More precise energy models lead to better building design outcomes
• Forecast accuracy — Improved predictions enable proactive energy management and planning
• Decision-making quality — Enhanced insights support better strategic and operational decisions

Data requirements

• Building design parameters (Structured) — Used to simulate and predict energy consumption patterns
• Historical energy consumption data (Numeric) — Provides baseline for training predictive models
• Sensor data from building systems (Numeric) — Feeds real-time operational data for dynamic modeling
• Building code documents (Text) — Used to verify compliance and guide design constraints

AI methods and techniques

• Predictive AI — Forecasts energy consumption and operational needs based on historical and real-time data
• Generative AI — Automates compliance verification and generates design recommendations
• Symbolic AI — Integrates domain knowledge and causal inference for explainable decision support

AI models and model families

GPT-4o, LLaMA 3, Custom Symbolic Neural Networks, Physics-Guided Memory Networks

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: E ONSE, Pacific Northwest National Laboratory.