EESCO – Vietnam Energy Efficiency Solution is an energy technology startup from Vietnam, focusing on optimizing the performance of water cooling systems (chillers) – one of the largest energy consumers in commercial buildings and industrial plants. Established with the goal of promoting sustainable development, EESCO has developed an intelligent modeling software platform that allows for monitoring, analyzing, and optimizing the operation of chiller systems.
EESCO’s solution can help cooling systems improve efficiency by 10% to 25% through operational optimization alone, and even achieve up to 40% improvement when combined with equipment renovation and upgrades. This not only leads to significant cost savings for businesses but also extends the lifespan of the system, while supporting organizations in achieving green transformation goals and long-term cost savings.
It is estimated that the energy optimization market for cooling systems in Vietnam has a potential demand of over 1.6 billion kWh per year. Therefore, EESCO aims not only to provide solutions for individual businesses but also to create a wider impact, helping to reduce CO₂ emissions, protect the environment, and promote the sustainable development of society as a whole.
At the P4G Vietnam Summit 2025, EESCO received support from the National Startup Support Center (NSSC) in organizing a booth to showcase its water cooling system optimization solutions. This also presented a key opportunity for the company to connect with investors, international partners, and the business community looking for energy-saving and sustainable development solutions. The support from NSSC has enabled this startup to introduce its green technology potential to the global market, opening up opportunities to scale and drive the green transition in the cooling industry.
EESCO’s intelligent solution is being seen as a practical step in helping businesses in Vietnam and the region effectively implement green transformation by directly addressing energy-intensive areas that have traditionally been under-optimized.
