Download - Load Forecasting Process Review Calvin Opheim Generation Adequacy Task Force October 7, 2013
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Load Forecasting Process Review
Calvin Opheim Generation Adequacy Task ForceOctober 7, 2013
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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
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Weather Zones
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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
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Previous Model Forecast Accuracy
Summer Peak MW Percent ErrorForecast Vintage 2011 2012 2013 2011 2012 2013
2012 Forecast 67,998 -1.1%
2011 Forecast 66,195 67,168 0.5% 0.1%
2010 Forecast 65,206 66,658 68,265 4.5% -0.2% -1.5%
Actual Peak 68,305 66,548 67,245
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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.
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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.
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Previous Model – What we’ve learned
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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.
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Neural Network Model Diagram
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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.
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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.”
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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
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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
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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.
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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
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Questions
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