This is like a weather forecast, but for how much energy a building will use. It learns from past data about the building (design, materials, historical meter readings, weather) and then predicts future consumption so you can plan and optimize better.
Manual energy planning and rule-of-thumb estimates lead to oversized systems, higher operating costs, and missed efficiency opportunities. By accurately predicting building energy consumption, architects, engineers, and facility managers can design more efficient buildings, size HVAC systems correctly, and run them in a way that reduces energy bills and emissions.
Access to large, high-quality datasets of building performance and local weather, combined with domain-specific feature engineering (e.g., occupancy profiles, envelope characteristics) and integration into established building design and management workflows.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Data availability and quality for diverse building types and climates; model retraining and calibration effort across many individual buildings.
Early Majority
Focus on data-driven, predictive energy modeling (beyond static rule-based simulations) that can adapt to real operational data, potentially offering more accurate and dynamic forecasts than traditional building energy simulation tools.