Smart Building & Energy

Smart Building & Energy is an AI-powered energy intelligence and decision-support platform designed to help building owners, facility managers, municipalities, campuses and other organizations improve energy efficiency, reduce operating costs and support decarbonization goals.

The platform combines building energy monitoring, deterministic engineering calculations, predictive analytics and AI-assisted decision support in a single integrated environment. Its main purpose is not only to visualize energy consumption, but also to identify abnormal behavior, forecast future demand, explain the likely causes of inefficiencies and recommend measurable improvement actions.

The proposed solution integrates data from smart meters, HVAC systems, lighting, equipment loads, occupancy information, indoor environmental sensors, weather data and, where available, renewable energy generation and storage systems. A deterministic energy engine calculates key indicators such as total consumption, energy use intensity, peak demand, energy cost, CO₂ emissions and baseline performance. These calculations remain the authoritative source for engineering decisions.

On top of this deterministic layer, machine-learning models are used for 24–48 hour load forecasting, anomaly detection, pattern recognition and peak-demand prediction. The AI Energy Copilot then converts validated analytical results into clear, explainable and actionable recommendations. Importantly, the AI layer does not replace engineering calculations or invent savings figures; it explains verified results and supports human decision-making.

The Challenge PoC is focused on a building-level demonstration. It will include an executive energy dashboard, smart meter and sensor data, HVAC and load breakdown, energy forecasting, peak-demand intelligence, anomaly detection, optimization recommendations, what-if scenario analysis and an AI Energy Copilot. A synthetic but engineering-realistic building dataset will be used to demonstrate scenarios such as after-hours HVAC operation, abnormal peak loads, inefficient temperature setpoints, weekend base-load problems and renewable energy variations.

The platform is designed to scale progressively from a single building to multi-building portfolios, campuses, districts and ultimately smart-city environments. Future modules can include PV and battery optimization, retrofit decision support, building digital twins, campus energy management and Positive Energy District applications.

Smart Building & Energy aims to transform building energy data into trustworthy, explainable and actionable decisions, enabling pilot partners to achieve measurable improvements in energy performance, cost efficiency and carbon reduction while maintaining a clear path toward wider smart-city integration.