Improve gross to net generation regression

[grgmiller]

Generally, our linear equation for regressing gross to net generation seems to fit the data very well (in some cases almost exactly). However, in the future we may want to consider refining our gross to net generation regression to see if any additional factors help explain some of the variation in the data. These factors could include:

• [ ] Investigate annual fixed effects
• [ ] Determine if/when major change in equipment (repower, new environmental controls) affects gross to net generation
• [ ] Investigate monthly fixed effects
• [ ] Capacity factor
• [ ] Binary variable for months where unit is operating and months when it is not

[grgmiller]

Currently, these regression values are not currently used in the data pipeline, although the linear model seems to fit the data well (r2-adj > 0.9). See this documentation for background on this method. This is because:

• the calculated net generation, when summed to the annual level, will not exactly match the total volume of net generation reported in EIA-923.
• For some (sub)plants the intercept term is positive, which does not have an intuitive physical explanation (it would mean that a plant could have positive net generation even when gross generation is zero).

Here, it seems like the tradeoff is between a) ensuring that the total net generation exactly matches the reported total net generation, or b) ensuring that the hourly profile has a shape that is as realistic as possible.