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Algorithm could save millions in energy costs

Evercomm co-founder Ted Chen holding the wireless sensor chip

A team from Nanyang Technological University, Singapore (NTU Singapore), led by Assistant Professor Wen Yonggang from NTU’s School of Computer Engineering, has developed an algorithm that could help companies and factories cut their energy bills by as much as 10%.

The algorithm is able to analyse energy consumption by connecting to sensors in computer chips already found in equipment such as computers, servers, air conditioning systems and industrial machinery. Such computer chips are needed for a host of functions such measuring temperature, logging data traffic and monitoring the workload of computer processors.

By combining it with externally-placed sensors, such as those that monitor ambient temperature, the technology can integrate and analyse all the operational data and recommend energy-saving solutions with almost no upfront cost.

In a typical semi-conductor factory which produces computer chips and components for computers and mobile devices, the annual electricity bill can reach SGD$50million and more.

Ted Chen, co-founder and product architect of Evercomm Singapore, the company that has licensed the algorithm, said: “With NTU’s new analytic engine, such large semi-conductor factories and campuses could save up to SGD$1m a year without a need to change much of their hardware, and instead, tune their operation and time their energy usage.

“The new algorithm allows us to use the most cost-effective way to find out where we can save energy, and our performance can be guaranteed by using real-time data.”

Even without deploying external sensors, Evercomm claims it can achieve up to 5% energy savings for companies, which are facing increased regulations worldwide on their energy usage and resulting carbon footprint.

Evercomm is looking to expand its expertise into the data centre industry. It has successful deployed a pilot test at the NTU Green Datacentre, saving 5% of its monthly electricity bill.

“Our next challenge is to look into how we can deploy our energy saving analytics into apartments and housing estates,” Chen finished.

Tom Austin-Morgan

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