Smart Meters Denmark

Maria Rønde Holm mdh@dst.dk Metode & Analyse

Olav Grøndal ogd@dst.dk Metode & Analyse

Statistics Denmark

Work with data

Datasource danish elhub energinet.dk

Datahub 2013 – launched april 2016

Actors in the electricity market

2

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

3

Background data and consumption data

Background 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 )

Metering point ID

Amount & readtime

4

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

128.822 business numbers to metering point

adresses. Unique linking = 1 meter 1 adress

5

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: 58701 meters

2014: 135993 meters

2015: 775691 meters

6

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: 2229.7

2 person/ household : 3862.4

3 person/ household : 4603.77

4 person/ household : 5408.98

5 person/ household : 6322.3

7

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:

01-01-2013 / 30-01-2014 or 01-02-2014 / etc.:

- 27 - 33 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

8

Modelling household consumption

275.793 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

9

Summary statistics

Dstribution of number of children

min 1st q. Median Mean 3rd q Max 0 0 0 0,35 0 11

Distribution of number of adults

min 1st q. Median Mean 3rd q Max 0 1 2 1,67 2 37

Distribution of square meters

min 1st q. Median Mean 3rd q Max

4 81 109 116 143 1483

10

Farmhouse: 11.706 Attached house: 49.262 Summerhouse: 6581 Dormitory: 118 Detached house: 135659 Appartment: 55760

Conclusion preliminary – random effects model

n=260758 t=11-12 N=3126789

Controlling for time effects and individual effects

Intercept farmhouse: 1066 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 = -1128 kwh std. err: 9.7

- Summerhouse = -476 kwh std. err: 14

- Dormitory = -761 kwh std. err: 79

- Appartment = -1288 kwh std. err: 9.8

- Detached house = -975 kwh std. err: 8.5

11

Example

2 adults & 2 children in 110 sq. appartment = 902.8

2 adults & 2 children in 110 sq. Detached house = 1216.8

2 adults & 2 children in 110 sq. Attached house = 964.8

12

Example of hourly readings daily

13

Example of hourly readings quarterly basis

14

Example of hourly readings quarterly basis

15

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

16