Linking water consumption smart metering with census data to improve demand
management
D. LOUREIRO*, M. REBELO**, A. MAMADE, P. VIEIRA, R. RIBEIRO URB AN WATE R DIVISION, NATION AL CIVIL ENGINEERING LABOR A TOR Y
www.i-widget.eu
PRESENTATION OVERVIEW
1.
Why do we need to improve understanding about urban water consumption?
2.
What approach to follow?
3.
What do we obtain when we apply it to profile
individual domestic consumption?
IMPROVE UNDERSTANDING ABOUT URBAN CONSUMPTION AND KEY FACTORS: WHY?
To improve water supply system planning, design, operation &
maintenance
To inform end-users about inefficient uses and promote behaviour changes
To facilitate cooperation and awareness raising between consumers and the utilities
To promote efficiency use of water & energy resources
IMPROVE UNDERSTANDING ABOUT URBAN CONSUMPTION AND KEY FACTORS: WHY?
Urban water consumption: domestic, non-domestic, water losses Domestic consumption (indoor&outdoor) = water use+ leakage Domestic consumption characteristics is dependent on local conditions
• Socio-demographic factors
• Economic factors
• Consumption practices
• Climate
• Household devices
Lack of general approaches
to profile consumption
IMPROVE UNDERSTANDING ABOUT URBAN CONSUMPTION AND KEY FACTORS: WHY?
Smart metering technology
− Enables utilities& consumers to access to more accurate consumption data, in real-time or near real-time
− Provides the possibility to
inform about consumption both locally and remotely
Census data
− Source of socio-demographic data (buildings, dwellings, families, individuals)
− Data available in the public domain
− Data available at small and homogenous area level (∼ 300 households)
THE APPROACH
Consumption data
− Data source: smart metering systems, SCADA systems
− Spatial scale (individual, small areas, district metering areas)
− Temporal scale (time step - 15 min.)
− Monitoring period: 3 months
Census data
− Last census - 2011 (georeferenced socio-demographic data base)
− Spatial scale: census unit (300 households)
− Temporal scale: (time step: 10 years) Data collection Data
processing Data analysis Consumption profiling
Not applicable to urban areas with important changes throughout the time!
THE APPROACH
Consumption data
− Data normalization
− Outliers detection
− Data combination
Census data
− Data extraction
− Data combination
− Data statistics calculation
Building Dwelling Family Individual
Data collection Data
processing Data analysis Consumption profiling
THE APPROACH
Consumption statistics calculation
− minimum night consumption (leakage analysis)
− daily average consumption, daily consumption pattern (water uses)
− peaking factors (extreme values, design)
Consumption scenarios identification
− daily, weekly, seasonal
Consumption data classification
− cluster analysis
Data collection Data
processing Data analysis Consumption profiling
THE APPROACH
Census data reduction
− Principal component analyses
Consumption profiling
− Correlation analysis (spearman)
− Multiple regression analysis
− Decision trees
Approach successfully applied at DMA level (Loureiro, 2010; Mamade, 2013)
Data collection Data
processing Data analysis Consumption profiling
THE CASE STUDY: METERING & CENSUS DATA
311 households equipped with smart meters
Households included in 18 census areas (3 DMA)
0 10 20 30 40 50
3021400415 3021400416 3021400417 3021400418 3021400419 3021400518 3027300306 3027300308 3027300309 3027300310 3027300311 3027300312 3021400702 3021400704 3021400705 3021400706 3021400709 3027300303
Mean consumption (m3/household/month)
census area
Monthly consumption
0 5 10 15
DMA 1 DMA 2 DMA 3
THE CASE STUDY: CONSUMPTION DATA
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
3021400415 3021400416 3021400417 3021400418 3021400419 3021400518 3027300306 3027300308 3027300309 3027300310 3027300311 3027300312 3021400702 3021400704 3021400705 3021400706 3021400709 3027300303
Peak factor (-)
Statistical unit
Monthly peaking factor
0.00.5 1.01.5 2.0
DMA 1 DMA 2 DMA 3
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Minimum night consumption (l/(household.hour))
Minimum night Consumption
Data collection Data
processing Data analysis Consumption profiling
Census areas with detached houses
National average value:10 m3/(client.month)
THE CASE STUDY: CONSUMPTION DATA
0 2 4 6 8 10 12 14
0 200 400 600 800 1000 1200 1400 1600
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
Minimum night consumption [l/consumer.h)]
Daily mean consumption [l/(consumer.day]
Consumer ID
Daily average consumption - working days Minimum night consumption
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
01/03/2009 11/03/2009 21/03/2009 31/03/2009 10/04/2009 Consumption (m3/h)
Date
• Daily average (indoor) consumption: 245 l/(client.day)
• Average night consumption (leaks): 0.4 l/(client.day)
Data collection Data
processing Data analysis Consumption profiling
Semi-open/open tap
Leaky tap
THE CASE STUDY: CONSUMPTION DATA
0 0.5 1 1.5 2 2.5 3
0:00 2:00 4:00 6:00 8:00 0:00 2:00 4:00 6:00 8:00 0:00 2:00
Dimensionless consumption (-)
Peak in the night period
Peak in the morning-lunch period Peak in the morning period
0 0.5 1 1.5 2 2.5 3
00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Dimensionless consumption (-)
Pattern 1 Pattern 2 Pattern 3
• Daily consumption patterns were grouped
• Working days – pattern with “peak in the morning period” predominates (133 consumers)
• Weekends – daily patterns with lower variability
Data collection Data
processing Data analysis Consumption profiling
Working days Weekends
LINKING WATER CONSUMPTION SMART METERING WITH CENSUS DATA
Spearman's rho coefficient correlation
Rented middle size dwellings
Working middle size families
Working adults and school age
population
Working days daily patterns
I) Peak in the night period 0.71 ** 0.81 ** 0.82 **
II) Peak in the morning- lunch period 0.67 ** 0.68 ** 0.65 **
III) Peak in the morning period 0.69 ** 0.63 ** 0.74 **
Daily average consumption (l/client.day)
I) High [787 l/c.day, N=19] 0.20 ns 0.40 ns 0.33 ns
II) Medium-high [411 l/c.day, N=71] 0.64 ** 0.72 ** 0.55 *
III) Medium [251 l/c.day, N=93] 0.80 ** 0.79 ** 0.84 **
IV) Low [56 l/c.day, N=128] 0.79 ** 0.61 ** 0.89 **
Data collection Data
processing Data analysis Consumption profiling
FINAL REMARKS
In the case study, it predominates consumers with lower-medium consumption- 245 l/(client.day)
For working days, it predominates the pattern with “peak in the night period”
For this consumption characteristics, the key factors were:
− Rented middle size dwellings
− Working middle size families
− Working adults and school age population
The results indicate that consumers may have already some water efficiency awareness
Smart metering system allow detecting important inefficiencies (household leaks)
Census data allows obtaining a first understanding about domestic consumption
