Smart Building Energy Management

MicrocosmWorks can deploy an intelligent energy management layer that overlays existing BMS infrastructure without requiring rip-and-replace upgrades. A network of IoT sensors measuring temperature, humidity, CO2, light levels, and occupancy feeds a cloud-based AI engine that continuously adjusts HVAC setpoints, lighting intensity, and ventilation rates in real time. The platform learns each building's unique thermal characteristics, occupancy rhythms, and weather sensitivity to generate predictive control strategies that stay ahead of demand rather than reacting to it. A unified energy dashboard provides floor-by-floor, zone-by-zone consumption breakdowns alongside automated sustainability reports aligned with ENERGY STAR, LEED, and GRESB frameworks.

The platform is delivered over 10-12 weeks across four phases. Weeks 1-2 conduct an energy audit of existing BMS infrastructure, map legacy protocol landscapes (BACnet, Modbus, KNX), and design the sensor overlay and protocol gateway architecture. Weeks 3-6 deploy protocol translation gateways and IoT sensors across pilot floors, build the MQTT-based telemetry pipeline to the cloud analytics platform, and implement the occupancy intelligence engine fusing PIR, CO2, badge, and WiFi probe data. Weeks 7-9 train and deploy the reinforcement learning HVAC optimizer using historical thermal response data and weather forecasts, build the zone-level energy consumption dashboards, and integrate automated lighting control based on occupancy and daylight sensing. Weeks 10-12 validate energy savings against baseline measurements, configure the sustainability reporting console for ENERGY STAR and GRESB compliance, and deliver the platform with building operations team training.

• Legacy BMS Overlay, Not Rip-and-Replace: MW can deploy protocol translation gateways that speak BACnet, Modbus, and KNX natively, layering intelligent control over existing building infrastructure without the cost and disruption of replacing functional equipment.
• Reinforcement Learning for Predictive HVAC Control: The platform uses an RL agent trained on each building's unique thermal characteristics to pre-condition zones before occupancy and shed load during vacancy, staying ahead of demand rather than reacting to temperature complaints after the fact.
• Privacy-Preserving Occupancy Intelligence: MW can fuse multiple anonymous data sources (PIR sensors, CO2 trends, WiFi probes) to build zone-level occupancy heatmaps without tracking individual identities, delivering the granularity needed for optimization while respecting tenant privacy concerns.

• IoT Development — Sensor deployment, BMS protocol integration, and edge gateway configuration
• AI Development — Reinforcement learning for HVAC optimization and occupancy prediction models
• Digital Consulting — Energy audit methodology, sustainability strategy, and ESG compliance roadmap

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