Part of the energy manager’s job is to spot and rectify anomalously high consumption and in principle it would seem that the more data you have, the better placed you should be to detect waste. Unfortunately, it doesn’t always work that way. Anyone with even a moderate number of meters to track will struggle to identify which, if, any, are showing significantly abnormal consumption. The problem is magnified immensely if the meters provide half-hourly data. Even with graphic visualisation to help, it’s still hard to identify which cases warrant investigation.

The answer is to focus on the costs of deviations from expected consumption. And don’t worry about doing it more frequently than once a week unless you are a really large user, in which case daily might be appropriate. As long as you have the processes in place to compute expected consumptions, as explained in the fifth of these bulletins, the methodology is trivial: price up all the kWh deviations and list them in descending order of cost. And that’s pretty much all there is to it. I call this concept the Overspend League Table. If you have the calculations set up in a spreadsheet, just feed in the consumption and driving-factor figures, and in an instant you will know exactly which cases to look at and what you’re likely to save by resolving them.

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