Du Smart Metering au Big Data

Transcription

1 Du Smart Metering au Big Data Séminaire CRESITT / S2E2 / CAP TRONIC Solutions pour l analyse et la gestion de la consommation électrique 19 Mai 2016

2 Smart Meters in France By 2021, Linky smart meters should be installed in almost every home They are foreseen, so far, to deliver 10-minute load curves 13 nov. 11 déc. 09 jan. What information can such data deliver? An important issue for the next few months and years!

3 Electric space and water heating Electric space heating o 31% of French homes (principal residences) use electric heating Electric water heating o ~45% of French homes (12 millions) o Most are set automatically by a load control switch a low frequency message (sent via the grid) turns them on at off-peak hours o Some water heaters are running on an as-needed basis these heaters are not addressed by the scope of this presentation

4 Validation: 12 households with ground truth readings Total consumption sensed at the main electricity meter Uno sensor (Vity) reads the meter s flashing light (1 flash per Wh) 10-second resolution + Submetering of individual loads Circle sensing plugs (Plugwise ) for space heaters and water heater 1-minute resolution We downgrade the data into 10-minute averages to simulate future Linky data

5 Disaggregation of space and water heating from real-life 10-minute load curves Illustration

6 Outcome for Household #12

7 Outcome for Household #2

8 Disaggregation of space and water heating from real-life 10-minute load curves Algorithm

9 Outline of the global algorithm At each 10-min data point (t k, P k ) we estimate the disaggregated values (X i ) 1 i n via constrained minimization of a cost function J: X 1, X 2,, X n = Argmin J(X i ) i n with J(X i ) i = i=0 (E i X i )²/Var i subject to X i = P k The expectations E i and the variances Var i need to be estimated beforehand from specific treatments on each household 10-min dataset o o n i=0 For space heating, a thermal gradients model relating consumption to external T, delivers heating expectations; the spread of cold days residuals enables estimation of its variance For water heating, the time-profile of the non-thermal estimated consumption (viz. the intercept of the thermal gradients model) enables estimation of the water heating expectations; the spread of residuals at the same time of day permits to estimate the variance

10 Thermal gradient General principle Looking for a linear relationship between electric consumption and outside temperatures W T C Power T C o Lagged T among regressors so as to catch inertia of the building o An additional day/night explanatory variable prevents confusion between heating and lighting (especially around 19:00) o Heating depends on hour of the day => 144 models computed, one for each 10-min slot

11 Illustration of a regression result W/ C W Water heating Water heating Hour in the day Instantaneous T -sensitivity 12 hour lagged T - sensitivity Sensitivity to previous T, with exponential smoothing Off-peak period Hour of day Off-peak period

12 Disaggregation of space and water heating from real-life 10-minute load curves Validation

13 Results: Household #3 Ground truth Estimation Bias: -13W, RMSE: 522W Bias: -112W, RMSE: 414W

14 Results: Household #12 Ground truth Estimation Bias: -96W, RMSE: 446W Bias: -35W, RMSE: 512W

15 Results: Household #1 Ground truth Estimation Bias: -79W, RMSE: 379W Bias: 37W, RMSE: 305W

16 Accuracy Household n Heating bias W Heating RMSE W Water heating bias W Water heating RMSE W # # # # # # # # # # # #

17 What for Disaggregation of costs: a strong demand from utilities In-flow disaggregation of 10-15' load curves Evaluation of heating performance: implications for insulation policies/subsidizing Smarter thermostat without additional heating consumption measurements Anomaly detection monitoring of homes, especially when not occupied by owners

18 Currently working on Other appliances: HVAC Lighting Off-peak-hours triggered water heaters at other times of the day Other types of water heaters Pool pump Dynamic consumption forecast: Withdrawing the assumption that 10 time slots are independent from each other Combining the statistical approach in the present paper, with dynamic modeling.

19 A Unique Positioning STATISTICS, MATHS, MODELS ALGORITHMS 5 DATA 10 + DATA AD HOC ANALYSES ENERGY MANAGEMENT SERVICES CUSTOMER INTERFACES MONITORING TOOLS FOR ENERGY SUPPLIERS MEASUREMENT ON HOUSEHOLDS SAMPLES, TARGETING ENERGY CONSUMPTION USAGES ELECTRICITY / GAS, 1 1h Load curve disaggregation Thermal gradients model Forecast (30, month, years) Anticipation of peaks Prediction of churn, Detection of devices Demand response potential Customer engagement Interactive advice Simulation tools Comparison vs. references Consumer databases creation Gaming Permanent households panel PowerMetrix Ad hoc samples recruitment, equipment and measurement Demand Response Testing Evaluation of the savings impact of a new equipment Before/after & with/without

20 Our Assets From energy consumption data to meaningful technology and marketing Patented disaggregation algorithms: meter load curve / electrical appliances load curve IT Big Data architecture Cassandra, Kairos, Kafka, Scala, MongoDB, Akka Streams, Google cloud, Hardware experience from the projects of the last 3 years Meter sensors, smart plugs, electricity & gas Team of high level data scientists, big data engineers and seasoned entrepreneurs

21 21 References