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# Improving prediction methods to estimate power curtailment

Improving prediction methods to estimate power curtailment. Saki Kinney Professor Eric Suess Department of Statistics, CSUH CSU Annual Student Research Competition May 3-4, 2002. Background: Curtailment Programs. Energy demand management has long been of interest Maintain power reliability

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## Improving prediction methods to estimate power curtailment

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1. Improving prediction methods to estimate power curtailment Saki Kinney Professor Eric Suess Department of Statistics, CSUH CSU Annual Student Research CompetitionMay 3-4, 2002

2. 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”

3. 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

4. Average Method Prediction = Average of the same hour over the previous ten business days 789 kW Curtailment

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

6. Temperature Variation

7. 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.

8. Analysis Overview • Compare overall prediction accuracy for different models to ISO average method • “Goodness of fit” • Compare curtailment estimates during actual power emergency

9. Other Prediction Models • RegressionModel • Power vs. Temperature • AutoregressionModel • Predict from previousvalues

10. Regression Method • Prediction = a + b*Temperature • Use previous ten days power and temperature data to get model for each hour 1534 kW Curtailment

11. 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

12. Autoregression Method 1032 kW Curtailment

13. Which Model is Better? • Regression and Autoregression models appear better than the Average model • Comparable to each other • Not conclusive – small sample size

14. Model Comparison

15. 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)

16. July 3rd Power Emergency

17. Impact on Performance • A higher baseline leads to higher performance estimate • Performance= Estimated Curtailment Promised Curtailment • Averaging model tends to understate actual performance

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

19. 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

20. Acknowledgments Cal State HaywardStatistics Department Lawrence Berkeley LabEnvironmental Energy Technologies Division

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