Data management

While the TESLA model uses information intensively, it depends on its internal resources for most of the variables it uses. In most field applications, there are only two kinds of update requirements: weather history and forecast, and the load history. Both of these requirements can be automated. For model development and estimation, we desire a five year history of hourly or sub-hourly load and hourly weather observations.

For most accurate forecast operation, the model requires access to a 30-day weather history and a 7-day load history. The load history is used in a final-stage error correction filter, and should be kept as close to current as possible. The weather history is used in the model's look-back computation, in order to capture the effects, for example, of a sustained period of abnormally hot or cold weather.

Common problems

Probably the most common problem with historical data is phasing errors: observations recorded against the wrong time. The most frequent error is a one-hour displacement, most often seen following the change from standard time to daylight savings or British summer time.

We have methods to detect these errors over time, but they cannot readily be discerned from one or two observations.

 

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