Controls & Automation
Honeywell Launches Autonomous Building Sustainability Solution
The cloud-based, closed-loop, machine learning solution continuously studies a building's energy consumption patterns and automatically adjusts for optimal savings without compromising occupant comfort levels.
March 4, 2020 By Energy Manager Canada
Honeywell has launched Forge Energy Optimization, a cloud-based, closed-loop, machine learning solution that continuously studies a building’s energy consumption patterns and automatically adjusts to optimal energy saving settings without compromising occupant comfort levels.
The company is calling this the first autonomous building solution focused on decreasing energy consumption, saying it may deliver double-digit energy savings, decrease a building’s carbon footprint, and can be implemented without significant upfront capital expenses or changes to a building’s current operational processes.
Honeywell points to a pilot project at Hamdan Bin Mohammed Smart University (HBMSU) in Dubai, United Arab Emirates, where the optimization solution demonstrated an initial 10% energy savings.
The system was applied to HBMSU’s existing building management system, which uses competitor technology to demonstrate the platform’s open architecture and hardware-agnostic capabilities.
The modern building is regarded as a highly smart, energy efficient building with fully connected lighting, cooling, building management, power and efficiency control that is optimized based on real-time occupancy. The pilot uncovered local control issues with the chiller plant and fresh air handling unit that were not adjusting to set points.
“Buildings aren’t static steel and concrete – they’re dynamic ecosystems and their energy needs fluctuate based on ever-changing variables like weather and occupancy,” said David Trice, vice president and general manager, Honeywell Connected Buildings. “With Honeywell Forge Energy Optimization, we’re evolving building operations far beyond what would be possible even with a robust team of engineers and the rules they code in their building management system. By employing the latest self-learning algorithms coupled with autonomous control, we can help building portfolio owners fine-tune their energy expenditures to drive efficiencies and create more sustainable practices for our customers.”
Honeywell’s solution autonomously and continually optimizes a building’s internal set points across hundreds of assets every 15 minutes to evaluate whether a building’s HVAC system is running at peak efficiency. When it finds a need to make an adjustment, it analyzes factors such as time of day, weather, occupancy levels, and dozens of other data points to determine the optimal settings per building and makes calculated decisions 96 times per 24-hour period for every building in a portfolio.
According to Honeywell, traditional HVAC control solutions incorporate varying levels of sophistication. The most basic involve static set points that don’t account for variable factors such as occupancy or weather. The second, and most common, rely on scheduled set-point adjustments using estimated occupancy and climate conditions. Finally, set points can be managed by a certified energy manager; however, most facilities have not found this solution to produce a viable return on investment due to the sheer volume of variables involved and the difficulty in producing accurate calculations in any scalable manner.
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