Product Announcement: Honeywell’s Machine Learning Solution Helps Fight Rising Energy Consumption in Buildings

(Hamdan Bin Mohammed Smart University in Dubai, United Arab Emirates, where Honeywell Forge Energy Optimization demonstrated an initial 10% energy savings. Credit: Honeywell)

by | Feb 26, 2020

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(Hamdan Bin Mohammed Smart University in Dubai, United Arab Emirates, where Honeywell Forge Energy Optimization demonstrated an initial 10% energy savings. Credit: Honeywell)

Honeywell announced today the launch of its 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, according to the company.

Dubai Pilot Program

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 optimized 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.

According to Honeywell, employing the self-learning algorithms coupled with autonomous control can help building portfolio owners fine-tune their energy expenditures to drive efficiencies and create more sustainable practices for our customers.

Achieving Energy Savings Beyond Standard Techniques

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.

Honeywell Forge Energy Optimization 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 Honeywell’s solution 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, 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.

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