Honeywell has launched Honeywell 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. Honeywell Forge Energy Optimization, the first autonomous building solution focused on decreasing energy consumption, 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.
During a pilot at Hamdan Bin Mohammed Smart University (HBMSU) in Dubai, United Arab Emirates, Honeywell Forge Energy Optimization demonstrated an initial 10% energy savings. HBMSU is the first accredited smart university in the UAE and is known for its technology and innovation programs.
Honeywell Forge Energy Optimization 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 additional energy savings is especially significant because HBMSU is regarded as a highly smart, energy efficient building with fully connected lighting, cooling, building management, power and efficiency control that is optimised based on real-time occupancy. The pilot also uncovered local control issues with the chiller plant and fresh air handling unit that were not adjusting to set points.
“As a smart university, we look to deploy the latest technology across our campus and ensure our buildings are efficient. We were pleasantly surprised by the results we saw from Honeywell Forge and its ability to drive further energy savings beyond our achievable optimisation with the techniques we have,” said Dr. Mansoor Al Awar, chancellor of Hamdan Bin Mohammed Smart University. “Our further partnership with Honeywell will help to support the advancement of artificial intelligence (AI) modeling for building automation and provide our students with first-hand applications of how AI and machine learning (ML) will drive operational efficiencies in buildings. Our goal is to collaborate with leading organisations like Honeywell that support our vision of educating the innovators of tomorrow.”
“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.”
Energy consumption in commercial buildings is a significant issue because these buildings account for more than 36% of global final energy consumption and nearly 40% of total direct and indirect CO2 emissions. Additionally, heating, ventilation and air conditioning (HVAC) often presents the largest opportunity for energy savings in a commercial building.
The system autonomously and continually optimises 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 Honeywell’s solution finds a need to make an adjustment, it analyses 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, 365 days a year across the system of assets. Repeated results have shown double-digit reductions of HVAC-related consumption while not impacting customer comfort.
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.