1. ERCOT PUBLIC 10/7/2013 1 Load Forecasting Process Review Calvin Opheim Generation Adequacy Task Force October 7, 2013

2. ERCOT PUBLIC 10/7/2013 2 Outline Long-Term Load Forecast Process Review Previous Model –Approach –What we’ve learned New Modeling Approach –Approach –Weather Normalization 4 Coincident Peak Analysis Questions

3. ERCOT PUBLIC 10/7/2013 3 Weather Zones

4. ERCOT PUBLIC 10/7/2013 4 Previous Model 2-3 weather stations per weather zone Used non-farm employment to capture future growth Weather Zone Forecasts –Daily Energy Per Job = f(weather, season, day type, daylight) –Hourly Demand = f(temperatures, previous hour’s load) ERCOT Forecast –∑ eight weather zone loads

5. ERCOT PUBLIC 10/7/2013 5 Previous Model Forecast Accuracy Summer Peak MWPercent Error Forecast Vintage201120122013201120122013 2012 Forecast67,998-1.1% 2011 Forecast66,19567,1680.5% 0.1% 2010 Forecast65,20666,65868,2654.5%-0.2%-1.5% Actual Peak68,30566,54867,245

6. ERCOT PUBLIC 10/7/2013 6 Previous Model – What we’ve learned Historical values of economic data are subject to significant revision for two years –During the first quarter of 2013, the Bureau of Labor Statistics increased non-farm employment values by 1% in 2011 and 2% in 2012. While values may seem small, relative impacts are significant. –Changing historical data compromises the accuracy of the model as “historical” relationships are subject to change. –Model was based on the assumption that non-farm employment values were stable.

7. ERCOT PUBLIC 10/7/2013 7 Previous Model – What we’ve learned Historical revisions impact forecast years –Moody’s forecast for CY2013 was increased by 2% in order to align with the revised historical values for CY 2012. Did load suddenly increase by 2% due to these revisions? Economic forecasts have been trending high, resulting in forecasts that reflect overly optimistic growth scenarios.

8. ERCOT PUBLIC 10/7/2013 8 Previous Model – What we’ve learned

9. ERCOT PUBLIC 10/7/2013 9 What’s new? Daily energy forecasts are now based on Neural Network Models. –Growth is determined by multiple factors (premise growth rates, weather variables, day types, and their interactions). –A single economic variable has less influence on forecast outcome. Benefits –ERCOT can determine/account for variable interactions more robustly, compared to linear regression models. –All predictor variables are used as inputs in each network node. –This approach produces more detailed/precise model formulation.

10. ERCOT PUBLIC 10/7/2013 10 Neural Network Model Diagram

11. ERCOT PUBLIC 10/7/2013 11 What’s new? Forecasts will now be based on many model simulations instead of being based on a single linear model. –Neural Network models were developed with 33% of the historical data being withheld from model development. –The data being withheld was determined randomly. –Randomly withholding data mitigates over-fitting of the data. –The model’s accuracy was determined based on how well it predicted the sample holdout data. –Process was repeated hundreds of times (model convergence). Benefits –In statistics, repeated sampling gives a more accurate estimate than a single sample. –The result is a more robust forecast.

12. ERCOT PUBLIC 10/7/2013 12 What’s new? Historical energy relationships will now be based on premise counts by customer class (residential, commercial and industrial). –Historical energy relationships will no longer be based on non- farm employment values. Benefits –Historical premise accounts will be very stable and will not be subject to the significant changes exhibited by non-farm employment revisions. –“Historical values are actually historical.”

13. ERCOT PUBLIC 10/7/2013 13 What’s new? The determination of 15-year normal forecast will now be based on model output using the most recent 15 years of historical weather data. –Will no longer create a synthetic weather file for use in the model –Will no longer time align weather conditions for time of peak Benefits –More accurately reflects historical weather patterns –More accurately reflects load diversity at time of peak

14. ERCOT PUBLIC 10/7/2013 14 2011 Summer Peak – Impact of 4 CP load reduction 4 CP impact shown is based on aggregated transmission load values for ~430 premises. Estimate is not based on an analysis of individual premises. Difference represents the 4 CP impact of ~600 MW on an aggregated basis. 4 CP impact would likely be greater if analysis were performed on individual premises. 4 CP impact

15. ERCOT PUBLIC 10/7/2013 15 2012 Summer Peak – 4CP Impact 4 CP impact 4 CP impact shown is based on aggregated transmission load values for ~430 premises. Estimate is not based on an analysis of individual premises. Difference represents the 4 CP impact of ~900 MW on an aggregated basis. 4 CP impact would likely be greater if analysis were performed on individual premises.

16. ERCOT PUBLIC 10/7/2013 16 2013 Summer Peak - 4CP Impact 4 CP impact shown is based on aggregated transmission load values for ~440 premises. Estimate is not based on an analysis of individual premises. Difference represents the 4 CP impact of ~500 MW on an aggregated basis. 4 CP impact would likely be greater if analysis were performed on individual premises. 4 CP Impact

17. ERCOT PUBLIC 10/7/2013 17 Questions ON OFF