1. Smart Meters Denmark Statistics Denmark
Maria Rønde Holm Metode & Analyse
Olav Grøndal Metode & Analyse
Statistics Denmark

2. Work with data Datasource danish elhub energinet.dk
Datahub 2013 – launched april 2016
Actors in the electricity market

3. Work flow in Statistics Denmark
First delivery of data march 2013
Type of datasets:
Background data 73 variables
Periodic readings: quarterly/monthly
Hourly readings
1.: step address cleaning and linking to registers
2.: Inditified types of matches
3.: getting an overview of timing of reading who has periodic and who has hourly

4. Background data and consumption data
Costumer info:
Metering point ID
Address, postal code identity number/ business number
Subscription information
supplier name, grid name (not necessarily the same) tarif (hourly or monthly/quarterly)
Consumption data (periodic / hourly )
Amount & readtime

5. Background data – status and challenges
Statistics Denmark can identify 98,4 % of the adresses in the background data
The business unit in Statistics Denmark can link business numbers to metering point adresses. Unique linking = 1 meter adress

6. Number of meters Periodic consumption datasets: Monthly / quarterly
2013: 3.18 mio. meters
2014: 3.23 mio. meters
2015: 3.25 mio. meters
Hourly consumption datasets:
2013: meters
2014: meters
2015: meters

7. Consumption households no model
2013: Number of people pr household. Number of people living in household at the end of the year
1 person/ household:
2 person/ household :
3 person/ household :
4 person/ household :
5 person/ household :

8. Modelling household consumption
The consumption dataset is read either monthly or quarterly, but not necessarly the same dates. But all dates appear in the readings dataset.
In order to link the right periods to person register neat dates were choosen. Example:
/ or / etc.:
days since last reading then the sum of the amount over the three month were grouped into a quarterly sum.
Select only the ones that appeared almost every month with a 31 day interval

9. Modelling household consumption
meters in the preliminary analysis
Time: January 2013 to december 2015
Model: consumption per quarter.
Model 1: number of adults/ household – fixed effect
Model 2: number of adults/ household & time effects – fixed effects
Model 3: number of adults/household, number of children & time effects – fixed effects
Model 4: number of adults/household, number of children, usage, sq. meter & time effects – random effects

10. Summary statistics Farmhouse: 11.706 Attached house: 49.262
Dstribution of number of children
min
1st q.
Median
Mean
3rd q
Max
0,35 11
Distribution of number of adults 1 2
1,67 37
Distribution of square meters 4 81
109
116
143
1483
Farmhouse:
Attached house:
Summerhouse: 6581
Dormitory: 118
Detached house:
Appartment:

11. Conclusion preliminary – random effects model n=260758 t=11-12 N=3126789
Controlling for time effects and individual effects
Intercept farmhouse: kwh std. err: 11.1
One ekstra adult = 211 kwh std. err: 0.8
One ekstra child = 122 kwh std. err: 1.05
One ekstra sq. m. = 4.18 kwh std. err: 0.04
Usage:
Attached house = kwh std. err: 9.7
Summerhouse = kwh std. err: 14
Dormitory = kwh std. err: 79
Appartment = kwh std. err: 9.8
Detached house = kwh std. err: 8.5

12. Example 2 adults & 2 children in 110 sq. appartment = 902.8
2 adults & 2 children in 110 sq. Detached house =
2 adults & 2 children in 110 sq. Attached house = 964.8

13. Example of hourly readings daily

14. Example of hourly readings quarterly basis

15. Example of hourly readings quarterly basis

16. Further work Further use of application
Indicator in economic cycle (nowcasting)
Identification buidling/construction site
Classify types of households – behavioral patterns.
New variable: High consumer / low consumer
Cluster analyse – hourly readings
… etc