A BEST distributor in the US has used realtime energy data via the Eniscope to identify a substantial maintenance and energy wastage issue, within just 4 weeks of the project starting.
It emerged that Bethune University was originally attracted to the Eniscope as a means to illustrate an irregularity in the power supply coming in from the utility transformer. The University had experienced many power surges over the past several years which cost them $10,000 per occurrence in blown water pumps and circuit boards. The Utility Company had been called out to evaluate the problem on several occasions and had always concluded that the surge must have been generated by a lightning strike and that the transformer was not in any way faulty.
So, the Eniscope was installed to record the next power surge. It was hoped that it would happen on a clear day, so the Utility Provider couldn’t blame the weather. However, within days of the installation, Eniscope was able to diagnose the root of problem which had gone undetected for 5 years!
When the original water chilling equipment was installed, the system was set to perform an automatic system shut down when the chiller reached a certain minimum load. The campus engineer was unaware of this setting. The Eniscope revealed that when the call for cold air across the campus was minimal, usually in the late night and the early hours of the morning, the system was actually shutting itself down and then restarting a few hours later. This caused a massive
energy spike at each start up and undue wear and tear on the 30 and 50 hp pumps being used to move the cold water from one building to the next.
Our Distributor brought this information to the attention of campus engineers. In turn they were then able to work with the chiller equipment service company to tweak the equipment settings and avoid the late night shut downs and subsequent full system start ups. This generated savings of over 30% per month in that building alone. This example clearly illustrates that you can not place a value on energy data until it has been acquired and evaluated. In this case, the energy
data was instrumental in identifying a real problem which was ultimately a very simple “no cost” adjustment to correct.