Big Data Europe 2nd Workshop in Enegry, Brussels 4/10/2016

1. Energy efficiency - big data challenges from
case studies
Jozef Stefan Institute Maja Skrjanc maja.skrjanc@ijs.si
BDE 2sd Workshop for Energy, Brussels 4/10/201616/6/2015
Company
Logo

2. 4-oct.-16www.big-data-europe.eu
 Big data in energy:
o Going green, Cutting back, Energy preservation
 Energy efficiency case studies (NRG4Cast, SUNSEED):
o Districts, buildings, households (monitor, analyze, test,
predict, optimize)
o Measurements (consumption, grid)
Outline

3. Big Data & Energy
 One of the hottest topics today is energy:
o consumption, discovery and implementation
o renewable, reusable and affordable energy, both at an individual and
business level
 Energy saving – standard of living (e.g. 2000W society):
o right energy-efficiency measures, districts can reduce energy use and costs,
and shrink buildings’ environmental footprint.
4-oct.-16www.big-data-europe.eu

4. Energy perservation
 Cutting-back:
o energy consumption - monitored and improved, companies can improve
efficiency and reduce expenditures.
 Going green:
o real-time and batch processing analytical tools evaluate:
 current green strategies and
 assess if those strategies are actually working and other areas that they can change to
green
o With increasing penetration of Distributed Energy Resources (DER) the smart
grid needs more & deeper monitoring and control to maintain stable operation
4-oct.-16www.big-data-europe.eu

5. Analysis of Environmental domain
 Common challenges:
o Different data sources
(structural data, sensor
measurements, annotations)
o Loads od data (history, on-
line sensor measurments,
various prediction models,
various forecasts, etc)
 Modern technology available:
o Amount of data is too large to be stored: new evidence from the incoming data is
incorporated into the model without storing the data

6. Sustainable energy management system
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7. NRG4Cast project
 NRG4Cast - real-time management, analytics and forecasting software pipe-line
for energy distribution networks :
o using information from network devices, energy demand and consumption, environmental
data and energy prices data.
 generic framework able to control, manage, analyze and predict behavior in an
extensible manner on other energy networks:
o gas distribution, heat water distribution and alternative energy distribution networks.
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8. Current and Expected impact
 Economic/Social
o Energy consumption savings up to 20%
o Dynamic energy tariffs – new jobs
o Lower energy bills for consumers up to 10%
o Saving in operational and maintain costs up to 15%
 Environmental
o Reduced CO2 emission up to 20%
o Saves on energy production up to 10%
4-oct.-16www.big-data-europe.eu

9. Three pillars of NRG4Cast
Monitoring & Prediction
of Consumption
and Production
Monitoring & Prediction
of Consumption
and Production
Prediction of electricity
prices
Prediction of electricity
prices Textual pipelineTextual pipeline
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Prediction of various impacts on the energy networks (accurate models)
Prediction of energy production of Renewable energy sources
Data fusion and requirements synergy

10. Integrated pilot
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11. NRG4Cast scenarios

12. Multimodal Stream Data Analytics
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13. Textual pipeline
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14. Architecture
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15. Achievements I
 NRG4CAST Ltd
 Final NRG4Cast Prototype (6 diverse pilots, 1 integrated pilot) – validation
on mass instalation
 Analytics:
o Prediction and stream modelling pipeline – semi-automatic
o Route Cause Analysis (RCA) module – novel approach to understand complex multi-
level multi-sensor system
o Framework for energy managements systems - MSDA (Multimodal Stream Data
Analytics). Hybrid approach by combining knowledge-driven and data-driven
elements
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16. Achievements II
 Data Access and Integration (DAI) platform (cca 800 data streams):
o DAI platform has evolved into a completely new system, that provides reliable access
to the pilot data at all times and is able to re-stream this data to other components
in the NRG4CAST platform
 Textual pillar:
o Although the practical value of achievements in the field of textual data analysis has
not been significant, the NRG4CAST project proposed an innovative way to handle
fact extraction from the textual stream
 Numerous SW testings (different components, different maturity levels)
 Stream modeling pipeline - integration of many different heterogeneous
data sources
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17. Challenges
 Technical Challenges:
o Data integration:
 Integration of real-time and static data - design the schema for the metadata database
 Integration of real-time data coming from hundreds of sensors (time-aligmenent)
 Variety of data interfaces for multimodal data
o Stream modeling pipeline - integration of many different heterogeneous data
sources
o HW installation
o How to reach TLR7 level of SW maturity
o Numerous SW testings (different SW components, different maturity levels)
o Defining appropriate features for prediction models
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18. Lessons learned
 Domain knowledge is the key (also in solving tech challenges)
 Input from business perspective necessary to push and drive
product development:
o market analysis,
o bussines plans
 Cyclic technical development (one prototype each year) turned
out to be winning combination
 Intensive dissemination activities are necessary
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19. SUNSEED project
 enable end-user to actively
participate in dynamic market
 to allow an operator to have
complete control over the smart grid

20. SUNSEED main objectives
Establish practical, converged DSO-
telecom, secure communications
network
Develop advanced measurement
&control sensor node
WAMS
Use intelligent analytical and
visualisation tools to manage
smart distribution grid resources
Large scale field trial
~ 1000 nodes
New business models of
converged DSO-telecom
infrastructure

21. SUNSEED project - Motivation
 Changing nature of the Consumers (households or industry) ->
Prosumers
o energy generators from renewable sources (photovoltaics, wind, cogeneration)
o manageable loads
 Utilities are „blind“ in LV distribution grid
o real-time monitoring is needed

22. Motivation (cont.)
 Manage risks related with network operation
o voltage violations, congestions, …
 Increasing hosting capacity of additional DER into existing grid
without additional reinforcements
 Offering new services for customers
 More efficient network operation
o increasing network observability, controllability and management

23. SUNSEED Architecture

24. SUNSEED Architecture

25. Com. solutions
Data flow

26. Delegated security management

27. Monitoring & Analytics & Control
 State estimation of distribution smart grids
 Forecasting
 Prediction of failures
 Active Network Management

28. State estimation of dist. smart grids
 Key enabler of advanced services
 WLS with Gauss-Newton
iteration scheme
 Linear Bayesian
estimation

29. Short Term Load Forecasting
 Load forecasts - on various nodes of DSO in the grid (end users, transf.
stations), for various forecasting horizons (1h – 24h).
 Data sources - load measurements, load estimations, weather status and
forecasts, static data (working hours, holidays, …)

30. Short term wind gener. forecasting
 propose an efficient SVM based multi-stage forecasting
technique incorporating pattern matching for data pre-
processing.

31. Fault Detection in Telco’s data
 Spatio-temporal model
• To detect and localize potential
faults in telco and DSO
network
 Outcomes
• Usual methods (plotting upload and
download speed matrix over time, analysing
histograms, probability distributions) do not
show enough structure
• Multidimensional scaling embeddings shows
more structure

32. Challenges
 Various communication protocols
 HW development
 HW elements are expensive, communication as well
 Minimal set of measurement nodes at locations to maintain
whole grid observability
 Integration of different security levels
 Huge potential – where to start with monetarization ?
(various stakeholders)
4-oct.-16www.big-data-europe.eu

33. Business models
Utility & telecom operator CO OP business models for
communication nets in distribution smart grids

34. Summary
 Wide range of opportunities:
o Environmental data, Behaviour data (grid, consumers), Social & Economy
o Knowledge discovery (monitor, understand, predict, optimize)
o Business models
 Technical challenges:
o Multimodal data integration, Data models
o Maturity of SW components, integration, support & maintenance
4-oct.-16www.big-data-europe.eu

35. Thank you!
Maja.Skrjanc@ijs.si
https://sunseed-fp7.eu/
http://www.nrg4cast.org/
4-oct.-16www.big-data-europe.eu