AI-Driven Lighting Automation with Daylight Harvesting and Blind Control

Static lighting schedules waste energy and compromise occupant comfort. This custom automation workflow replaces them with a dynamic, multi-input control system. It ingests real-time data from ambient light sensors, astronomical clocks, and BMS occupancy signals to calculate the optimal blend of electric light and natural daylight. The operational upside is direct: a 40-60% reduction in perimeter lighting energy costs and a measurable decrease in HVAC cooling load from managed solar heat gain, translating to a rapid ROI.

Implementation integrates directly with DALI or KNX lighting networks and blind controllers, managed by a central orchestrator built on frameworks like LangGraph for stateful control logic. Critical controls include tenant override interfaces via building apps, which route through a human-in-the-loop approval gate to preserve comfort, and comprehensive observability logging to BMS historians for performance validation and continuous optimization of the control algorithms against actual energy savings.

This workflow automates perimeter zone lighting and solar heat gain by maintaining target lux levels. It eliminates the manual calibration and static schedules of traditional systems, directly reducing lighting energy consumption and HVAC cooling loads. The operational upside comes from continuous, sensor-driven optimization that responds to real-time occupancy, sun position, and external glare, turning a fixed cost center into a dynamic, efficiency-generating asset. Savings are measurable in reduced kilowatt-hours and extended equipment lifespan.

Implementation integrates a LangGraph orchestrator that processes triggers from astronomical clocks, IoT sensors, and BMS schedules. It deploys specialized agents for harvesting and glare control, which issue low-level commands via DALI or KNX protocols to fixture and blind controllers. Critical controls include tenant override interfaces via building apps, fail-safe defaults, and comprehensive observability dashboards for performance and exception monitoring. The architecture is designed for phased rollout, starting with pilot zones before portfolio-wide deployment.

A successful portfolio-wide deployment begins with a controlled pilot in a single perimeter zone. This phase validates the core orchestration logic—integrating DALI/KNX lighting controls, motorized blinds, ambient light sensors, and astronomical clock scheduling—within a live BMS environment. The goal is to prove the 40-60% energy savings thesis, establish baseline lux level performance, and refine tenant override interfaces via building apps before committing to broader capital expenditure.

Post-pilot, rollout is sequenced by building readiness and ROI potential. Automated configuration tools, fed by BIM and BMS point lists, accelerate agent deployment. A centralized observability layer—built on tools like Grafana and integrated with CMMS for maintenance alerts—provides portfolio-wide performance dashboards and exception monitoring. This phased approach de-risks the investment, ensures operational stability, and delivers compounding energy savings across the asset base.

A successful rollout begins with a governance framework that defines decision authority, exception handling, and performance monitoring. A central orchestration layer, typically built with LangGraph or a custom state machine, must integrate with the building's DALI/KNX lighting controllers, BMS for HVAC coordination, and astronomical clock services. This layer enforces tenant comfort policies, manages override requests from building apps, and logs all actions for auditability. The primary control is a closed-loop system where ambient light sensors provide real-time feedback to dimmable LED drivers and motorized blind actuators, maintaining target lux levels while minimizing energy draw and solar heat gain.

Phased deployment mitigates risk. Phase 1 establishes sensor calibration and baseline data collection in a single perimeter zone, validating the forecasting model against actual daylight. Phase 2 rolls out closed-loop control to all perimeter zones, integrating blind control and implementing tenant override interfaces via the building app. Phase 3 adds predictive logic using weather APIs and connects the system to the BMS for holistic load management. Each phase includes a parallel run period where the AI system's commands are shadowed against the legacy schedule, with automated A/B testing to quantify energy savings and occupant satisfaction before full cutover.