Weather Correction in the TESLA Model

The TESLA model will yield historic load corrected for weather effects. That is, it will produce an estimate of the load that would have occurred if "normal" weather had prevailed, and therefore identify that portion of the load that is due to weather variations. The same factors permit scenario-type evaluations of the effect of future weather patterns on load.

The primary weather variables used in the TESLA model include temperature, wind speed and direction, cloud cover, humidity, and precipitation, and the interaction among these factors and between them and other load determinants.

Weather Interactions

The effects of an aspect of weather (say temperature) depends on many other factors, including other weather conditions (is it raining, is the wind blowing etc). The model explicitly captures relevant interactions, which typically include

Time of day
Season of the year
Calendar events (holidays, weekends, etc)
Other weather measures
Angle of the sun

Isolation of Weather Effects

Estimating the effect of weather on load requires complete specification of all the weather variables and their interactions, and of the delays in their effects. It also requires that other factors correlated with weather phenomena be carefully accounted for as well, to prevent contamination of the weather effect estimates through cross correlations.
To correct for weather, and to analyze the effects of weather deviations on a contingent basis, there is no substitute for a completely specified model that gives explicit weight to the entire available weather data base.

Modeling Approach in the TESLA System

The TESLA model is estimated using the hourly measures available on weather variables reported by the Weather Service. Interactions among these and other variables are then calculated and entered into the data set, along with a lag structure that captures delayed effects. Experience has shown that delays of more than a day (and longer) are common before the full effects of a particular weather event are felt.

 

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