Temperature correction of energy consumption time series
This presentation is the property of its rightful owner.
Sponsored Links
1 / 24

Temperature correction of energy consumption time series PowerPoint PPT Presentation


  • 67 Views
  • Uploaded on
  • Presentation posted in: General

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.

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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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

Final consumption of energy – natural gas

  • Energy consumption depends strongly on air temperature – so it is seasonal


Average monthly temperatures

Average monthly temperatures

  • But temperatures do not exhibit perfect seasonality


Seasonal adjustment in x12 arima

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


Seasonal adjustment in x12 arima1

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


Temperature correction coal

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


Current method its effect

Current method – its effect


Current method its effect1

Current method – its effect


Regression in x12 arima

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


Regression in x12 arima1

Regression in X12-ARIMA

  • Why won’t the following work?


Regression in x12 arima2

Regression in X12-ARIMA

  • So we use this:


Regression in x12 arima3

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


Regression in x12 arima4

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


Coal estimated coefficients

Coal – estimated coefficients


Interpreting the coefficients

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!


Interpreting the coefficients1

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


Coal estimated coefficients1

Coal – estimated coefficients


Gas estimated coefficients

Gas – estimated coefficients


Smoothing the coefficients for coal

Smoothing the coefficients for coal


Comparing seasonal adjustments

Comparing seasonal adjustments


Heating degree days

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


Smoothing the coefficients heating degree days model eurostat measure

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


Effect on coal seasonal adjustment

Effect on coal seasonal adjustment


The difference temperature correction can make

The difference temperature correction can make!


  • Login