Verdigris Technologies > Case Studies > AI-based Automation for Commercial Office HVAC: A Verdigris Case Study

AI-based Automation for Commercial Office HVAC: A Verdigris Case Study

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 AI-based Automation for Commercial Office HVAC: A Verdigris Case Study - IoT ONE Case Study
Technology Category
  • Sensors - Environmental Sensors
  • Sensors - Temperature Sensors
Applicable Industries
  • Buildings
  • Cement
Applicable Functions
  • Facility Management
  • Maintenance
Use Cases
  • Building Automation & Control
  • Occupancy Monitoring
  • System Integration
The Customer

Fortune 500 commercial office

About The Customer
The customer in this case study is a Fortune 500 company that operates a mixed-use office and laboratory building. The company was seeking a solution to optimize the energy efficiency and comfort of their building's HVAC system. The building runs for extended hours, supports a variety of uses, and contributes to significant economic productivity. The company was facing challenges with their existing building and environmental control systems, which were failing to adequately support facilities and operations management. The building was inefficient and the people using it were underserved.
The Challenge
Modern buildings are required to run longer hours, support a variety of end uses, and contribute to higher levels of economic productivity, leaving a thin margin for error. However, even the most advanced building and environmental control systems have failed to adequately support facilities and operations management. Buildings are often inefficient and the people using them are underserved. To meet occupant comfort and maintain cost and energy efficiency, a dynamic, AI-assisted approach is needed.
The Solution
Verdigris has developed an AI-based solution to automatically minimize HVAC energy usage while maintaining occupancy comfort. Verdigris sensors monitor the building’s energy usage thousands of times per second. To create forecasts, the AI incorporates local weather, utility pricing, building management system (BMS) data, and other available datasets. Where data may not be explicitly available, Verdigris AI can infer it. In this simulation, Verdigris’ AI learns the building’s occupancy by observing security badging patterns. The solution simulates the results of an automatic and persistently optimizing heating ventilation and air conditioning (HVAC) system for a Fortune 500 operated building.
Operational Impact
  • The AI-based solution developed by Verdigris was able to significantly optimize the HVAC system of the Fortune 500 company's building. The solution was run with three parameter selections: optimizing for temperature stability, optimizing for occupant responsiveness, and a balance of both. All models indicated the potential to optimize the current HVAC systems. For instance, HVAC systems were found to operate at levels more than necessary to maintain a comfortable occupant environment during the weekends when occupancy is minimal. Under Verdigris AI-optimized HVAC operations, compliance with comfort objectives increased to 100% for both the stable temperature and balance objective scenarios. In an occupant responsiveness optimization scenario, there were some time intervals during which the temperature may fall outside comfort zone guidelines, but all of those occurred during times the AI engine forecasts little to no occupancy.
Quantitative Benefit
  • Persistent automated HVAC energy savings up to 18.7%
  • Persistent automated HVAC cost savings 22.7-33.7%, depending on optimization criteria
  • Increase from 4.5% of “occupied hours” within ASHRAE 55 standard to a persistent automated 100% of time within optimal productivity performance

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