MAKING SMART METERING SMARTER BY APPLYING DATA ANALYTICS

Transcription

1 MAKING SMART METERING SMARTER BY APPLYING DATA ANALYTICS Georges HEBRAIL, Senior Researcher Electricité De France (EDF) R&D Nancy, September 18th, 2014

2 AGENDA 1. EDF GROUP AND EDF R&D 2. SMART GRIDS AND SMART METERS 3. SMART GRID DATA ANALYTICS APPLICATIONS 4. FOCUS ON SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION 5. CONCLUSION ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/2014 2

3 INSTALLED CAPACITY EDF GROUP A GLOBAL LEADER IN ELECTRICITY 75.6 billion in sales 39.1 million customers GENERATION 158,467 employees worldwide 85.1% generation without CO 2 3

4 ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/2014 4

5 EDF GROUP R&D employees including : 370 PhD 150 PhD students 200 researchers teaching at universities and advanced engineering schools More than 500 million budget in % activity to support performance of Group businesses 15 departments (expertise, partnerships and project management) 14 joint research laboratories Partnering with 5 venture capital funds in the field of clean technologies 10 international Centres including 3 France 7 Germany, United Kingdom, Poland, China, Italy, USA, Singapour 30 % activity to anticipate and prepare for the future ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

6 EDF GROUP R&D RESEARCH PROGRAMS A unique R&D division for all Group businesses Generation Energy management Customers and sales Renewable energies Electrical networks Information technology ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

7 AGENDA 1. EDF GROUP AND EDF R&D 2. SMART GRIDS AND SMART METERS 3. SMART GRID DATA ANALYTICS APPLICATIONS 4. FOCUS ON SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION 5. CONCLUSION ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/2014 7

8 Source : European Technology Platform for the Electricity networks of the future A Smart Grid is an electricity network that can intelligently integrate the actions of all users connected to it - generators, consumers and those that do both (+ storage) - in order to efficiently deliver sustainable, economic and secure electricity supplies A smart grid employs innovative products and services together with intelligent monitoring, control, communication, and self-healing technologies ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/2014 8

9 WHAT IS A SMART GRID? The bad news: more complexity in the power grid Low CO2 emission pressure : integration of renewable energies wind, solar generation which are local and show high variability Economical pressure : facing the increase of consumption with minimum investment Peaks of consumption have to be reduced Evolution of customer indoor equipment and usage ex. electric vehicle Still maintaining reliability and quality The good news: ICT can help A computer network can be added to the electricity network Better control/supervision of the network by real time messages More sensors are being installed on the electricity network ex. smart meters Household appliances will have communicating facilities Internet of things ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/2014 9

10 Source: Intelligrid EPRI ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

11 SMART METERS Source : CRE (Regulatory Commission of Energy) - ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

12 SMART METERS ERDF Linky smart meter specifications Remote command of electricity supply and power limits Fraud detection Measurement of consumption and production Recording and transmission of consumption (power / energy) Local display and local data access Management of supply contract and billing Temporal sampling rate: 10 24h Latency: one day ahead Source : CRE and ERDF ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

13 SMART METERS Source: Utility AMI Analytics for the Smart Grid : Applications, Markets and Strategies, Zach Pollock, Greentech Media, June 2013 ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

14 SMART METERS Smart metering data eco-systems On the grid side Network topology and equipments Sensors on different locations and equipment Weather information, On the home/building side Description of building Equipment, appliances, Energy consumption by usage, internal/external temperature, ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

15 AGENDA 1. EDF GROUP AND EDF R&D 2. SMART GRIDS AND SMART METERS 3. SMART GRID DATA ANALYTICS APPLICATIONS 4. FOCUS ON SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION 5. CONCLUSION ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

16 SMART GRID DATA ANALYTICS APPLICATIONS GTM Research distinction between Grid analytics utilize the advanced data recording capabilities of smart meters to determine the status and condition of assets that are situated in the last mile of the distribution grid Consumer analytics consumer analytics [is] the ability to leverage data in order to gain new insights into customer behavior to use in load planning and demand response, reducing financial losses due to theft and poor billing processes, and minimizing the value-at-risk created through customer churn in deregulated markets Source: Utility AMI Analytics for the Smart Grid : Applications, Markets and Strategies, Zach Pollock, Greentech Media, June 2013 ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

17 SMART GRID DATA ANALYTICS APPLICATIONS Source: Utility AMI Analytics for the Smart Grid : Applications, Markets and Strategies, Zach Pollock, Greentech Media, June 2013 ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

18 SMART GRID DATA ANALYTICS APPLICATIONS ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

19 SMART GRID DATA ANALYTICS APPLICATIONS Forecasting electricity demand (EDF R&D with IBM Research Dublin) Prévision nationale Prévision par ACR Prévision par poste(s) HTA/BT Module d agrégation Module de prévisions adaptatives ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

20 SMART GRID DATA ANALYTICS APPLICATIONS Forecasting electricity demand (EDF R&D with IBM Research Dublin) Use cases Managing a large number of local forecasts Managing slow evolution of electric power consumption Managing network reconfigurations ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

21 SMART GRID DATA ANALYTICS APPLICATIONS Forecasting electricity demand (EDF R&D with IBM Research Dublin) Reference: Massive-Scale Simulation of Electrical Load in Smart Grids using Generalized Additive Models, P.Pompey, A.Bondu, Y.Goude, M.Sinn, Lecture Notes in Statistics: Modeling and Stochastic Learning for Forecasting in High Dimension. 06/2014. ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

22 SMART GRID DATA ANALYTICS APPLICATIONS Energy efficiency in buildings / households Sources: ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

23 SMART GRID DATA ANALYTICS APPLICATIONS Energy efficiency in buildings / households Measure of electric power consumption by usage Estimation of electric power consumption by usage ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

24 SMART GRID DATA ANALYTICS APPLICATIONS Measure of electric power consumption by usage Smart Home and Internet of Things ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

25 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION Estimation of electric power consumption by usage Disaggregation algorithms from global building metering See: Is Disaggregation the Holy Grail of Energy Efficiency? The Case of Electricity, Armel, K. C., Gupta, A., Shrimali, G., & Albert, A., Energy Policy, 52, (2013). ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

26 AGENDA 1. EDF GROUP AND EDF R&D 2. SMART GRIDS AND SMART METERS 3. SMART GRID DATA ANALYTICS APPLICATIONS 4. FOCUS ON SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION 5. CONCLUSION ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

27 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION Importance of knowing electric power consumption at different levels of aggregation Load planning Demand response Urban planning Crossing electric power consumption data with external data What if we had the electric power consumption available As a continuous function (temporal dimension)? For each individual building / industry (spatial dimension)? We could aggregate at any temporal and spatial level Ex. electric power consumption in a city district every hour Almost irrealistic even in the future! ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

28 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION The X-DATA collaborative funded project in France ( ) Industrial partners Academic partners Startups Goal Identify valuable data among partners + open data Identify use cases of data sharing with a business model Develop a technical platform to store and process data in a sharing perspective ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

29 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION The X-DATA collaborative funded project in France ( ) Crossing territory time series describing activity and/or moves Data examples Time series of number of cell phone calls Time series of electric power, water consumption Time series of moves from cell phones Use case examples Forecasting of activity, of consumption, of tourism Prediction of large crowds, traffic Social weakness of a neighborhood Requires data at the same spatial and temporal divisions! ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

30 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION Case of electric power consumption Available data: electric power consumption at MV Feeder level every 10 Required data: electric power consumption at IRIS level every hour or day HV/MV substation INSEE IRIS MV Feeder MV/LV substation ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

31 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION Detailed available data Electric power consumption of several MV feeders (ex. 600 around Lyon city area) observed for a long time (3 years) Network topology (geographical position of every substation connected to each MV feeder) Geographical positions of IRIS borders Weather data (ex. T) and calendar variables (ex. day of the week) for the period Demographic and economic information at the IRIS level (official statistics) ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

32 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION Naive solution: population interpolation Each timestamp is considered separately IRIS population is distributed to MV feeders in proportion to n i,j MV feeders consumption is distributed equally to its population Y i,t = consumption of IRIS i at time t (unknown) Y j,t = consumption of MV feeder j at time t (known) n i,j = # of substations in IRIS i connected to feeder j ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

33 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION Solution based on forecasting models Consider several MV feeders observed for a long period Learn a forecasting model of the time series at MV feeder level z j,t : weather and calendar data at MV feeder level X j : estimated demographic and economic variables at MV feeder level Apply the forecasting model at IRIS level Dimension of X is large (almost 400) clustering of IRIS in 7 classes (k=1..7) association of a class to each MV feeder estimation of one model per class at MV feeder level ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

34 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION Use of Generalized Additive Models (GAMs) for class k ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

35 SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION Preliminary results Reference: Spatial Interpolation of Electricity Consumption Data using Socio-demographic Information, J.MEI, Y.GOUDE, G.HEBRAIL, EDF R&D internal report n H-E FR ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

36 AGENDA 1. EDF GROUP AND EDF R&D 2. SMART GRIDS AND SMART METERS 3. SMART GRID DATA ANALYTICS APPLICATIONS 4. FOCUS ON SPATIO-TEMPORAL ESTIMATION OF ELECTRIC POWER CONSUMPTION 5. CONCLUSION ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/

37 CONCLUSION Smart grids are necessary to manage with new renewable energies in an electricity distribution network Smart metering data begins to be available and can help in: Grid analytics Consumer analytics Interaction with Smart Homes and also with Smart Cities (not covered here) Several new problems arise for ML community Time series analysis: exploratory/predictive analytics Advanced analytics on «data at rest» and on «data in motion» ECML/PKDD G.Hebrail - Making smart metering smart by applying data analytics 09/