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Forecasting accuracy can be evaluated in terms of overall performance, and in
terms of predicting critical events. When forecasting electric load, the
critical event is most often peak load, both in timing and in the level of load.
The TESLA model provides outstanding results on both scores.
A commonly used standard of accuracy for a load model is the mean absolute
percentage error, or MAPE. In-sample, TESLA distribution system models in the
field demonstrate MAPEs in the range of 0.84 percent to 1.56 percent. These
figures are based on using the actual weather data, but without employing any
final-stage filters to update forecasts based on recent load experience. If
final stage filters are used to improve the forecast based on recent load data,
the MAPE will be reduced by an amount ranging from 0.15 percent to 0.30 percent.
If the basic model is used with forecast weather, our experience is that the fit
degrades by about 0.5 to 1.0 percent, depending of course on the accuracy of the
weather forecast. Note: These figures include weekends and holidays.
While TESLA is over-all an extremely accurate product, it performs particularly
well near peak usage times in predicting both the time and the level of load.
The VLLS approach offers great strength at critical turning points, particularly
peaks and troughs. In fact, TESLA tends to have lower average errors near peaks
than in general.
Forecasting accuracy like this enables us to offer TESLA on a
pay-for-performance basis. Under such an arrangement, the contract contains a
pay-for-performance schedule which varies TESLA’s compensation according to an
agreed-upon schedule. One basis for setting the compensation schedule, for
example, is a measure of savings to the client due to increased forecast
accuracy. TESLA Inc, will also negotiate a payment plan that is part fixed-fee
and part pay-for-performance. TESLA Inc welcomes opportunities to demonstrate
the superiority of the TESLA model through competitive benchmarks.
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