MACHINE LEARNING TECHNOLOGY FINDS ENERGY SAVING OPPORTUNITY - EP&T Global

EDGE Zeus Machine Learning technology autonomously compares energy data readings against those taken from days of similar temperature and humidity. Through similar temperature analytics, Zeus was able to identify an abnormal 80kW increase in Chiller load versus a comparable temperature day.

We notified the client of the above issue who in turn discovered the cause to be a faulty pressure sensor which was triggering the Chiller to draw higher current. The pressure sensor on the Chiller was replaced, resulting in normal usage resuming and saving our client £13,700 over a 6 month period.