The company said that the system has been proven to increase energy savings in commercial buildings by 10%. It said that commercial buildings are currently responsible for 36% of global energy consumption and nearly 40% of CO2 emissions, giving scope for substantial energy savings if efficiencies can be made.
The pilot programme of the Honeywell Forge Energy Optimization system was carried out with the Hamdan Bin Mohammed Smart University (HBMSU) in Dubai. Honeywell Forge said that it was able to drive 10% energy savings despite the campus already having impressive green operations.
Machine learning enables autonomous adjustment of the building energy settings. The technology analyses sensor data points from any building management system to determine whether a building鈥檚 system is properly optimised in each room. Once the system identifies a flaw or need for adjustment, it can analyse multiple factors 鈥� such as time, weather, occupancy and more 鈥� to determine a room鈥檚 optimal, energy-saving temperature.
The company added Honeywell Forge Energy Optimization can be implemented without significant upfront capital expenses or changes to a building鈥檚 current operational processes.
During the pilot at HBMSU, Honeywell Forge Energy Optimization demonstrated an initial 10% energy savings. The system was applied to HBMSU鈥檚 existing building management system - which uses competitor technology - to demonstrate the platform鈥檚 open architecture and hardware-agnostic capabilities.
Honeywell said that the 10% additional energy saving was especially significant because HBMSU鈥檚 building was already regarded as highly smart and energy-efficient, 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.
鈥淎s a smart university, we look to deploy the latest technology across our campus and ensure our buildings are efficient,鈥� said HBMSU chancellor Dr Mansoor Al Awar. 鈥淲e 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. Our further partnership with Honeywell will help to support the advancement of artificial intelligence (AI) modelling 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.鈥�
Honeywell Connected Buildings vice president and general manager David Trice added: 鈥淏uildings aren鈥檛 static steel and concrete 鈥� they鈥檙e dynamic ecosystems and their energy needs fluctuate based on ever-changing variables like weather and occupancy. With Honeywell Forge Energy Optimization, we鈥檙e 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.鈥�
Honeywell Forge Energy Optimization carries out autonomous and continual optimisation every 15 minutes to evaluate whether a building鈥檚 HVAC system is running at peak efficiency. When Honeywell鈥檚 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.
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