Weather Normalization: Model Selection and Validation EFG Workshop, Baltimore Prasenjit Shil

Transcription

1 Weather Normalization: Model Selection and Validation EFG Workshop, Baltimore Prasenjit Shil

2 Ameren at a Glance Ameren Missouri, Ameren Illinois and Ameren Transmission Company 2.4 million electric customers and more than 900,000 natural gas customers across 64,000- square-mile area Ameren Missouri ranks as the largest electric power provider in Missouri, and Ameren Illinois ranks as Illinois' third largest natural gas distribution operation in total number of customers Ameren Missouri has 10,200 MWs of Generation capacity. Ameren's rates are some of the lowest in the nation 2

3 Weather Normalization Model Primarily for regulatory purpose Uses only temperature as weather variable; Primary variables used: Two day weighted average temperature Multiple cut points/splines to model different weather responses using linear regression Models are built for most rate classes: residential/commercial/industrial Daily energy and peak models Detail models with other weather variables such as wind speed, cloud cover, heat index etc. are also built Used in unbilled analysis, primarily in summer months Each spline has different slope Load vs. Temperature plot: showing multiple spline points 3

4 Weather Normalization Model Model consists of typical variables Day of the week, weekend/weekday month, season, spline and their interactions. No AR/MA term used enabling the model to completely explain weather impact. Models are simulated using actual weather data and the residuals are added back when the models are simulated using normal weather which provides Normal load. Com SGS Examples of WN model Residential 4

5 Weather Normalization: Model Selection, Validation and Effectiveness Usual standard statistical criterion such as MAPE/MAD, AIC/BIC, R-squared/Adjusted R-squared, F-Statistic, Std. Error of Regression etc. are used to select the final model; Out of sample forecast statistics are also compared to select final model. Residual pattern (using residual graph and ACF/PACF charts) is analyzed. Actual vs. Predicted graphs are analyzed too. However, there is no easier way to analyze how well the model is responding to a range of weather observations when compared to actual load for each month. So, we use Excel ; other programming tools can be used to simulate the model. Perhaps Itron can include it in the next version of MetrixND 5

6 Weather Normalization: Validating Models and Understanding Model Fit Scatter plot is created with actual load data and temperature. The model is simulated using numerous temperature points (all ranges) for each month and plotted in the same scatter plot The simulated series creates load curve for a given month for various temperature points using the model specification. Simulated series is plotted along with actual load/temperature scatter plot. The resulting graph compares actual and simulated load. Time consuming process to create the simulated series. For a good model, generally speaking, the actual data points will be scattered closely around the simulated series. 6 These monthly example charts show actual vs. simulated load for a range of temperature

7 Weather Normalization: Validating Models The most important thing is to check if the regression line appears to pass through the center of the scatter plot at all temperatures. A tight scatter plot will imply a better fit (a higher R-squared value, which generally indicates a better model). But the nature of the data could just contain a lot of scattered points, which doesn't necessarily mean that it isn't a good model. Refer to the simulation spreadsheet to show the scatter plot for the Actual/Simulated Load vs. Temperature range. Although these data points are not close to the simulated series, the model is fitted well for this business class 7

8 Thanks

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