Smart Meters Denmark Statistics 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

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

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

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

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

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

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

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

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: 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

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

Example of hourly readings daily

Example of hourly readings quarterly basis

Example of hourly readings quarterly basis

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