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Regression Analysis for NDM Data: Impact of Weather & Calendar Variables on Consumption Patterns

This summary provides an in-depth analysis of the regression models applied to LDZ data, focusing on the contribution of weather and calendar variables. Key findings, including the impact of CWV, global radiation, time intervals, and more are discussed to enhance understanding and decision-making.

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Regression Analysis for NDM Data: Impact of Weather & Calendar Variables on Consumption Patterns

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  1. Option C: Regression Analysis Summary of NDM Data Sample Analysis

  2. Contents • Regression Analysis per LDZ • In-Sample Results • Out-of-Sample Model fit • CWV Contribution • Conclusion

  3. Regression Analysis • Regression Model as follows: • Dummy variables (Bank Holidays, Easter, Christmas and so forth). • Weather variables introduced as per DESC meeting on 4th April (e.g. Temperature, Global Radiation, Rainfall and so forth). • Time intervals used based on office hours and domestic habits. • Slot 1 from 5am to 8am • Slot 2 from 9am to 4pm • Slot 3 from 5pm to 10pm • Slot 4 from 11pm to 4am

  4. Regression Analysis • Data normalised by AQ because of erratic level changes observed year on year. Yearly cut-off date is of 1st April due to time span of original files and data deletion process • Binary permutation of variables used to seek out best regression fit with p≤5% significance level.

  5. Regression Analysis Models used • A benchmark model was used for each LDZ as the following: • Normalised Consumption= Intercept + a0 * CWV • Using Binary permutations, a most optimised linear regression model (based on best R2 fit) is chosen. The linear regression is of the form: • Normalised Consumption= Intercept + a0 * CWV + a1 * Temperature + a2* Windspeed + a3* Solar Radiation + … • In-Sample data runs from April 2008 to March 2011 whereas Out-of-Sample data spans from April 2011 to March 2012. • These models were applied to End-User Category 1 only (EUC1).

  6. Regression Analysis Parameters (1 of 2)

  7. Regression Analysis Parameters ( of 2)

  8. In-Sample MAPE Results

  9. In-Sample R2 Results

  10. Out-of-Sample MAPE Results

  11. Out-of-Sample R2 Results

  12. Analysis of Contribution of CWV in Optimised Models

  13. Conclusion • Improvements against Benchmark Results are made using weather and/or calendar effects on top of CWV. • The significance, or non-significance, level of Weekend/Weekday/Bank Holiday is very much LDZ-specific. • Global Radiation is a significant variable in all LDZ’s. • Time Intervals (i.e., Slot 1 to 4) and Monday-to-Thursday dummy variable help explain customer behaviour in some LDZ’s. • Relative Humidity stands out in almost every LDZ’s. • CWV heavily contributes in the optimised models obtained. • No cross-effects utilised in Regression models. • LDZ SO and NT need further investigations

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