This document discusses using data from multiple domains in smart cities to improve energy demand prediction. It proposes using data from mobility networks and patterns, in combination with energy and weather data, through a data market platform. This could allow more accurate predictions of energy demand, including for areas without direct energy monitoring. The data market approach aims to overcome barriers to data sharing by establishing standards for data curation, security and brokerage that respect privacy.

1. Data Market Austria
Energy Demand Prediction in Smart Cities:
A Cross-Domain Approach
Danilo Valerio
Siemens Corporate TechnologyPublic © Siemens AG 2017

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Agenda
• Data landscape in smart cities
• Energy and Mobility: A synergistic approach
• Energy demand prediction via DMA
• Conclusion

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Smart City: A world full of data
Public
Administration
Mobility
Others…
Smart
buildings
Multiple Visualization Tools and Applications
Operational Source Systems
Traffic
Energy Water
Waste
Healthcare
Multiple Loading & Streaming Tools

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Smart City: A world full of data silos
Others…
Multiple Visualization Tools and Applications
Operational Source Systems
Multiple Loading & Streaming Tools
Public
Administration
Mobility
Smart
buildings
Traffic
Energy Water
Waste
Healthcare

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Smart City: A world full of data silos
Others…
Multiple Visualization Tools and Applications
Operational Source Systems
Multiple Loading & Streaming Tools
Public
Administration
Mobility
Smart
buildings
Traffic
Energy Water
Waste
Healthcare
Why?
• Concerns of disclosing business-sensitive information
• Concerns of disclosing privacy-sensitive information
• Costs of cleaning, managing, and disseminating data
• Underestimated potential of data linking/fusion
• Misperception of the “value” of raw data

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A DMA Cross-domain Use Case
Public
Administration
Mobility
Others…
Smart
buildings
Multiple Visualization Tools and Applications
Operational Source Systems
Traffic
Energy Water
Waste
Healthcare
Multiple Loading & Streaming Tools

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Energy and Mobility: A synergistic approach
Smart mobility
Public Transport,
FCD, Sensors,
Census, Mobile
phone data, etc.
Smart grid
Smart transformers,
Smart meters,
GMDs,
Grid control center
Energy patterns
Energy demand Mobility demand
Mobility patterns
Energy
storage
Energy
distribution
Road/Traffic
planning
Road/Traffic
operation
Strong
Correlation [1]
[1] Tosi et. Al. “Big Data from Cellular Networks: How to estimate Energy Demand at real-time”, IEEE DSAA’15

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Data Market Austria
Energy and Mobility: A synergistic approach
Smart mobility
Public Transport,
FCD, Sensors,
Census, Mobile
phone data, etc.
Smart grid
Smart transformers,
Smart meters,
GMDs,
Grid control center
Energy patterns
Energy demand
Mobility patterns
Data provenance
Data curation Data preservation
Data security Data brokerage
Service brokerage
Data fusion

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Energy demand prediction via DMA
T-Mobile premises
Siemens premises
Network data
Transformer
stations
Aggregation,
filtering, and
preprocessing
Anonimization,
Aggregation, and
preprocessing
Cell coverage
Cell
Transformer coverage
Transformer

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Energy demand prediction via DMA
T-Mobile premises
Siemens premises
Network data
Transformer
stations
Aggregation,
filtering, and
preprocessing
Anonimization,
Aggregation, and
preprocessing N. of users in a cell
Cell coverage
Cell
Incoming rate
Outgoing rate
Transformer coverage
Transformer

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Energy demand prediction via DMA
T-Mobile premises
Siemens premises
Network data
Transformer
stations
Aggregation,
filtering, and
preprocessing
Anonimization,
Aggregation, and
preprocessing N. of users in a cell
Cell coverage
Cell
Incoming rate
Outgoing rate
Transformer coverage
Transformer
Transformer
Load

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Energy demand prediction via DMA
Example 1: Using mobility data to improve energy demand forecast
Prediction (hourly
for the next 24 hrs)
Energy Demand app
v Energy Data (80%)
Weather Data (+ 3%)
v Mobility Data (+ 7%)
Data Market
Energy
Weather
Mobility
Events

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Energy demand prediction via DMA
Example 2: Using mobility data to estimate energy demand in areas not monitored
Prediction (hourly
for the next 24 hrs)
Data Market
Weather
Mobility
Energy Demand app
Energy Data (NA)
v Mobility Data (65%)
Weather Data (+ 5%)

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Conclusion
• Barriers to data sharing should and can be overcome
• Data Market Austria paves to way towards collaborative business opportunities
• Synergies among domains can be leveraged
• Cross-domain use cases will be even more important in the future (e.g., with the spread of e-cars).
Next Steps
• Refining the Use Case and requirements specification
• Collection and integration of data
• Selection of features for the improved forecasting model

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Dr. Danilo Valerio
Senior Research Scientist
Corporate Technology
CT RDA BAM CON-AT
Siemensstrasse 90
1211 Wien
E-mail:
danilo.valerio@siemens.com
Internet
siemens.com/corporate-technology

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BACK-UP SLIDES

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Privacy-PreservationAgent(PPA)
Trusted Repository A
System-wise analytics
repositories
Integrated analytics repository and API exposure
Smart sensors A1, A2…
…
Privacy-PreservationAgent(PPA)
Trusted Repository B
System-wise analytics
repositories
Smart sensors B1, B2…
uint uext uint uext
Privacy-PreservationOrchestrator(PPO)
Privacy preservation
Privacy policies (OWL ontology)

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Privacy-PreservationAgent(PPA)
Trusted Repository A
System-wise analytics
repositories
Integrated analytics repository and API exposure
Smart sensors A1, A2…
…
Privacy-PreservationAgent(PPA)
Trusted Repository B
System-wise analytics
repositories
Smart sensors B1, B2…
uint uext uint uext
Privacy-PreservationOrchestrator(PPO)
Privacy policies (OWL ontology)
Privacy preservation
1.Legal constraints
2.User consent
3.Privacy criticality
4.Purpose of the analytics
task
5.API access policies
6.Linkability coefficient
(audit)
1. Legal constraints
2. User consent

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Privacy-PreservationAgent(PPA)
Trusted Repository A
System-wise analytics
repositories
Integrated analytics repository and API exposure
Smart sensors A1, A2…
…
Privacy-PreservationAgent(PPA)
Trusted Repository B
System-wise analytics
repositories
Smart sensors B1, B2…
uint uext uint uext
Privacy-PreservationOrchestrator(PPO)
Privacy policies (OWL ontology)
Privacy preservation
1.Legal constraints
2.User consent
3.Privacy criticality
4.Purpose of the analytics
task
5.API access policies
6.Linkability coefficient
(audit)
1. Legal constraints
2. User consent
1) Anonymization
• Removal
• Irreversible/Reversible pseudonymization
• Intra/Interdomain
2) Data aggregation
• Aggregation by subject
• Aggregation by time
• Aggregation by class membership
3) Data perturbation
• Additive
• Multiplicative