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Bde sc3 2nd_workshop_2016_10_04_p10_maja_skrjanc


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

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Bde sc3 2nd_workshop_2016_10_04_p10_maja_skrjanc

  1. 1. Energy efficiency - big data challenges from case studies Jozef Stefan Institute Maja Skrjanc BDE 2sd Workshop for Energy, Brussels 4/10/201616/6/2015 Company Logo
  2. 2.  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. 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. 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
  5. 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. 6. Sustainable energy management system 10/4/2016 6
  7. 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. 10/4/2016 7
  8. 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%
  9. 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 10/4/2016 9 Prediction of various impacts on the energy networks (accurate models) Prediction of energy production of Renewable energy sources Data fusion and requirements synergy
  10. 10. Integrated pilot 10/4/2016 10
  11. 11. NRG4Cast scenarios
  12. 12. Multimodal Stream Data Analytics 10/4/2016 12
  13. 13. Textual pipeline 10/4/2016 13
  14. 14. Architecture 10/4/2016 14
  15. 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 10/4/2016 15
  16. 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 10/4/2016 16
  17. 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 10/4/2016 17
  18. 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 10/4/2016 18
  19. 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. 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. 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. 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. 23. SUNSEED Architecture
  24. 24. SUNSEED Architecture
  25. 25. Com. solutions Data flow
  26. 26. Delegated security management
  27. 27. Monitoring & Analytics & Control  State estimation of distribution smart grids  Forecasting  Prediction of failures  Active Network Management
  28. 28. State estimation of dist. smart grids  Key enabler of advanced services  WLS with Gauss-Newton iteration scheme  Linear Bayesian estimation
  29. 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. 30. Short term wind gener. forecasting  propose an efficient SVM based multi-stage forecasting technique incorporating pattern matching for data pre- processing.
  31. 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. 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)
  33. 33. Business models Utility & telecom operator CO OP business models for communication nets in distribution smart grids
  34. 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
  35. 35. Thank you!