1 / 24

Temperature correction of energy consumption time series

Temperature correction of energy consumption time series. Sumit Rahman, Methodology Advisory Service, Office for National Statistics. Final consumption of energy – natural gas. Energy consumption depends strongly on air temperature – so it is seasonal. Average monthly temperatures.

donnan
Download Presentation

Temperature correction of energy consumption time series

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Temperature correction of energy consumption time series Sumit Rahman, Methodology Advisory Service, Office for National Statistics

  2. Final consumption of energy – natural gas • Energy consumption depends strongly on air temperature – so it is seasonal

  3. Average monthly temperatures • But temperatures do not exhibit perfect seasonality

  4. Seasonal adjustment in X12-ARIMA • Y = C + S + I • Series = trend + seasonal + irregular • Use moving averages to estimate trend • Then use moving averages on the S + I for each month separately to estimate S for each month • Repeat two more times to settle on estimates for C and S; I is what remains

  5. Seasonal adjustment in X12-ARIMA • Y = C × S × I • Common for economic series to be modelled using the multiplicative decomposition, so seasonal effects are factors (e.g. “in January the seasonal effect is to add 15% to the trend value, rather than to add £3.2 million”) • logY = logC + logS + logI

  6. Temperature correction – coal • In April 2009 the temperature deviation was 1.8°(celsius) • The coal correction factor is 2.1% per degree • So we correct the April 2009 consumption figure by 1.8 × 2.1 = 3.7% • That is, we increase the consumption by 3.7%, because consumption was understated during a warmer than average April

  7. Current method – its effect

  8. Current method – its effect

  9. Regression in X12-ARIMA • Use xit as explanatory variables (temperature deviation in month t, which is an i-month) • 12 variables required • In any given month, 11 will be zero and the twelfth equal to the temperature deviation

  10. Regression in X12-ARIMA • Why won’t the following work?

  11. Regression in X12-ARIMA • So we use this:

  12. Regression in X12-ARIMA • More formally, in a common notation for ARIMA time series work: • εt is ‘white noise’: uncorrelated errors with zero mean and identical variances

  13. Regression in X12-ARIMA • An iterative generalised least squares algorithm fits the model using exact maximum likelihood • By fitting an ARIMA model the software can fore- and backcast, and we can fit our linear regression and produce (asymptotic) standard errors

  14. Coal – estimated coefficients

  15. Interpreting the coefficients • For January the coefficient is -0.044 • The corrected value for X12 is • The temperature correction is • If the temperature deviation in a January is 0.5°, the correction is • We adjust the raw temperature up by 2.2% • Note the signs!

  16. Interpreting the coefficients • If is small then • So a negative coefficient is interpretable as a temperature correction factor as currently used by DECC • Remember: a positive deviation leads to an upwards adjustment

  17. Coal – estimated coefficients

  18. Gas – estimated coefficients

  19. Smoothing the coefficients for coal

  20. Comparing seasonal adjustments

  21. Heating degree days • The difference between the maximum temperature in a day and some target temperature • If the temperature in one day is above the target then the degree day measure is zero for that day • The choice of target temperature is important

  22. Smoothing the coefficients, heating degree days model (Eurostat measure)

  23. Effect on coal seasonal adjustment

  24. The difference temperature correction can make!

More Related