AI-controlled buildings. From start to self-optimized HVAC systems, now possible in weeks

Many real estate investors consider using modern software solutions, like AI, to enhance their existing manual HVAC systems. Companies are drawn to AI-managed HVAC systems due to the massive economic and sustainability benefits. However, they are afraid it is a major, time-consuming, non-scalable project, which has now proven not to be true.

Controlling and optimizing commercial and public buildings HVAC systems have historically been time-consuming manual work. Sometimes, the HVAC systems' manual processes have been expedited with the help of a SCADA system or on-site system. Nevertheless, it has become a real challenge to keep up with the rapid increase and development of systems and sensors in buildings during the last decades. Further, building owners face increased demand for flexible space usage, safe indoor conditions, and substantially reduced energy consumption ad CO2 emissions.

"The number of processes, measurement points, and data in BMS and HVAC systems that need attention has exploded in the last decade. As a result, traditional maintenance organizations that use manual processes find it impossible to manage HVAC systems properly," says Mikko Maja, CTO at Nuuka.

A perfect task for AI
Facility managers must meet the demand for reduced energy and CO2 emissions without jeopardizing the health of buildings and tenants. As a result, machine learning - or AI - is increasingly being applied to optimize existing building HVAC and energy systems. The benefits of using AI are massive both from an economic and sustainability perspective. AI learns the building's usage and continuously predicts and adjusts the HVAC operating system in real-time. Using AI, buildings have reduced energy consumption and CO2 emissions by up to 30% on average while simultaneously maintaining a healthy indoor climate inside the premises. Despite the strong results, the implementation of AI in existing commercial and public buildings has lagged. One reason could be the industry's expectation that the process will be long, complicated, and resource-demanding.

"To meet our customers' demand, we have developed an AI solution that can diagnose and optimize their building's HVAC in no time," Says Mikko Maja. "Thanks to our long history in developing software solutions for HVAC and our compatibility to all existing building HVAC and energy systems, we have managed to turn AI adoption to be quick. For most customers, the effective implementation time is 6-8 weeks".

The implementation of Nuuka's AI solution in an existing building is a three-stage process. First, Nuuka connects the building BMS system to the cloud and starts collecting the data. Secondly, Nuuka's software starts diagnosing the building's HVAC systems performance to detect faults and gives suggestions for correcting them before introducing AI software to the building. Finally, phase three involves the deployment of AI-based optimization to the HVAC system.

"We are very proud of the possibilities this brings to the real estate industry," says Mikko Maja, CTO of Nuuka. "Finally, real estate owners have tools that upgrade existing commercial and public buildings to be AI optimized future buildings."