LG Multi V i AI Energy Saving Project at ITB University

In ITB’s Labtek VI building, fluctuating occupancy and uneven solar heat gain created inconsistent temperatures and high energy use. The previous HVAC system often ran unnecessarily, prompting the need for a solution that could adapt in real time.

In ITB’s Labtek VI building, fluctuating occupancy and uneven solar heat gain created inconsistent temperatures and high energy use. The previous HVAC system often ran unnecessarily, prompting the need for a solution that could adapt in real time.

The AI engine analyzes eight key variables, such as temperature, humidity, air flow, and set temperature. Through machine learning, it calculates the time to reach the target temperature and fine-tunes compressor frequency, refrigerant flow, and fan speed. This enables rapid cooling when the load is high and energy savings when it is low, which is ideal for spaces with varying occupancy.

The AI Indoor Space Care function groups indoor units according to environmental similarity. Units work together to balance temperature across the room, automatically activating or deactivating as needed. In the case of this site, temperature differences stayed within one degree Celsius without manual adjustments, preventing overcooling and saving energy.

The performance of MULTI V i was monitored over a 90-day period, with the system alternating daily between Normal mode and AI mode. The Monthly Energy Consumption results clearly show that AI mode consistently required less energy to operate, even while maintaining stable indoor comfort across classrooms and laboratories.