Improving prediction methods to estimate power curtailment

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Improving prediction methods to estimate power
curtailment

• Saki Kinney
• Professor Eric Suess
• Department of Statistics, CSUH
• CSU Annual Student Research CompetitionMay 3-4,
  2002

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Background Curtailment Programs

• Energy demand management has long been of
  interest
• Maintain power reliability
• Reduce costs
• Programs provide incentive to reduce power when
  electricity is in peak demand
• 2001 Use your appliances after 7pm

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Measuring Demand Reduction

• Key component of incentive program is to
  determine participant performance
• Requires estimating what power would have been
  if a curtailment order had not been issued
• Curtailment Predicted Power- Actual Power
• Typically averaging methods are used

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Average Method

• Prediction Average of the same hour over the
  previous ten business days

789 kW Curtailment
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Average Method

• Turns out to be a poor prediction method
• Does not account for variables affecting power
  consumption
• Time (past consumption)
• Temperature

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Temperature Variation
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Temperature Variation

• Curtailments typically called for on hottest days
• New participants complained this put them at a
  disadvantage
• Performance measured against cooler days
• Past participants typically were not concerned
  with temperature variability.

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Analysis Overview

• Compare overall prediction accuracy for different
  models to ISO average method
• Goodness of fit
• Compare curtailment estimates during actual power
  emergency

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Other Prediction Models

• Regression Model
• Power vs. Temperature
• Autoregression Model
• Predict from previous values

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Regression Method

• Prediction a bTemperature
• Use previous ten days power and temperature data
  to get model for each hour

1534 kW Curtailment
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Autoregression Method

• Past power usage used to predict future power
  usage
• Use several days data, including current day, up
  to curtailment time
• Temperature variation is implicit
• Temperature data not required
• Time correlation is accounted for

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Autoregression Method
1032 kW Curtailment
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Which Model is Better?

• Regression and Autoregression models appear
  better than the Average model
• Comparable to each other
• Not conclusive small sample size

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Model Comparison
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Summer 2001 Results

• July 3rd Power Emergency
• Illustrate difference in curtailment estimates
  using different models
• Example Results from two Federal Buildings that
  participated in ISO program
• (Source US GSA, LBNL)

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July 3rd Power Emergency
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Impact on Performance

• A higher baseline leads to higher performance
  estimate
• Performance Estimated Curtailment
  Promised Curtailment
• Averaging model tends to understate actual
  performance

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Further Considerations

• Analyze data from different types of buildings in
  different climate zones
• Consider model variations
• Evaluate models used by other states and programs

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Conclusions

• Better predictive models are needed in demand
  reduction programs
• Fairness
• Reliability
• Decision making and planning
• Accounting for temperature variability and time
  dependence provides more accurate results

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Acknowledgments

• Cal State HaywardStatistics Department
• Lawrence Berkeley LabEnvironmental Energy
  Technologies Division

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Impact on Performance