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Half Hourly Data – an invaluable energy management tool

Half hourly data

Half hourly energy consumption data always forms the basis of our M&V Plans, allowing us to identify the most appropriate periods for ‘baseline’ or expected levels of consumption, but why is it so useful?

Half hourly data is a common form of interval energy data for large consumers of electricity – other intervals used may be every fifteen minutes, every twenty minutes or every hour, but half hourly data is the form we are most familiar with in the UK. The energy supplier will often supply an interval meter, in fact this is mandatory for consumers over a certain size. The data collected by these meters can be used to accurately determine the electricity bills based on how much and exactly when electricity is used.

An organisation may also choose to install a private metering system in order to monitor their energy consumption, and may have multiple meters installed in different parts of their building. Use of this type of ‘sub–metering’ is particularly useful in identifying where energy is used or wasted, and the more detailed the data, the better!

Baseload exceptions

Half hourly data is such a valuable energy management tool because it allows you to scrutinise your consumption at a high level of detail. For example, how do you know if everything that should be turned off overnight is actually turned off? The baseload is the lowest half hourly reading in a 24 hour period, usually recorded overnight and for buildings such as offices should be relatively consistent outside of operational hours. The example on the left shows a week with two exceptions in the overnight consumption. These exceptions resulted in an additional consumption of approximately 300 kWhs – with this information it would be possible to find out if this overnight consumption was necessary or avoidable.

The example below shows a fairly consistent baseload, but consumption first thing on Monday morning is much higher than the other days in the week. Why was this? The half hourly data tells us exactly when this peak occurred to within half an hour, so it may well be possible to identify which piece of equipment caused this peak and take steps to avoid future occurrences.

MD exceptions

It could be that peaks such as the one in the previous example are not exceptional – perhaps because of extremes in external temperature. However, this sort of exception reporting makes interval data highly valuable for ‘monitoring and targeting’.

There are many systems that are able to automate and report the identification of exceptions, generically known as AMT (Automatic Monitoring and Targeting) and certainly one of the steps we recommend taking to reduce energy consumption and carbon dioxide emissions.