KNIME UGM 08/03/2016

Tim WuytsEngineer Data Intelligence

Agenda

• introducing fifthplay

• use cases

• marketing analysis

• PV monitoring

• automating appliance detection

a member of Niko Group

Part of Niko Group

We believe that people deserve a modern

electrical installation that gives them COMFORT,

CONTROL and EFFICIENCY in a pleasurable way.

17070075% °1919

Market share in switches and

socket outlets in Belgium

Family-owned company

founded in 1919

170 mill ion euro turnover

+700 employees

Mission fifthplay

We provide connected solutions, based on a scalable IoT platform, and bring added value for partners and their customers.

Fifthplay platform

AP

I

LAN

Integration of third party hardware

… and many more

Packaged Solutions

Where do we use KNIME?

• Marketing Analysis• IoT Data Intelligence Features

• Data Exploration

• Automation

Marketing Analysis

Marketing Analysis - Workflow Example

Marketing Analysis – PPT ExampleDevice X

Monthsactive

25%

of

dat

a

25% of data

Box50%

TOTAL CUSTOMERS *697

Gw inactive 132

19%

• Out of 697 customers with a device of Type X connected to their gateway, 19% had their gateway inactive on the moment of analysis

• Analysis of these 19% (132 customers) reveals the following:

• 1 in 4 gateways (25%) was active up to 1 month before becoming inactive

• Half of the gateways became inactive within the first 4 months

• 75% was active for at most 8 months before inactivity.

• The longest period recorded before inactivity was 18 months.

• Some gateways were never active.

• Conclusion

• The gateways tend to become inactive very quickly.

• Idea: create triggers to detect customers whose gateway became inactive in the last month & take action (for instance: contacting them to check if there is an issue).

IoT data intelligence features

Alerting Oven, TV, ...Appliance Type Detection

Benchmarking

PV monitoring

Product improvement Self learning thermostat, TRV

. . .

More cases tocome

PV monitoring

The produced electricity from your solar panels is predicted based on historical profile, cloud index, radiation and temperature. When production is lower than expected, the owner is notified.

PV MonitoringPrediction Model Search

PV Monitoring‘Prediction’ Model Search

All PV installations in one single model ?

Time for KNIME

PV MonitoringPrediction Model Search

Loops + Linear/Polynomial Regression + Boxplot

PV MonitoringAnomaly Detection

• problem: training data !!

• only 1 known case of malfunction

• approach:

• find model that would have predicted the

known malfunction

• apply model to other installations

• investigate detected anomalies

Q: Is my PV installation still performingat the same performance level as before?

PV MonitoringAnomaly Detection

PV degradation started 01/2013

• failure started on 27/12• 40% of panels offline on 15/01• 60% of panels offline on 12/03• failure solved on 28/05

PV MonitoringAnomaly Detection Models

Day below prediction

PV MonitoringAnomaly Detection Models

Day below prediction – moving average

PV MonitoringAnomaly Detection Models

% Underperformance – moving average

Time for KNIME

PV MonitoringAnomaly Detection Models

• Applying the final model to all installations reveals

• 20 installations showed anomalies

• 4 installations had miscellaneous inconsistencies in the historic data

• 9 triggered an alert in the winter months

but production values were very low (< 100 Wh per day)

• 7 installations had an alert on the same day (28/12/2014)!

PV MonitoringSo what happened?

PV MonitoringSo what happened?

DEZE GEGEVENS MOGEN VRIJ WORDEN GEBRUIKT MITS DE VOLGENDE

BRONVERMELDING WORDT GEGEVEN:

KONINKLIJK NEDERLANDS METEOROLOGISCH INSTITUUT (KNMI)

STN = stationsnummer

YYYYMMDD = datum (YYYY=jaar MM=maand DD=dag)

RD = 24-uur som van de neerslag in tiende millimeters

van 08.00 voorafgaande dag- 08.00 UTC huidige dag

SX = codecijfer sneeuwdek om 08.00 uur UTC:

codecijfer sneeuwdikte

1 1 cm

... ...

996 996 cm

997 gebroken sneeuwdek < 1 cm

998 gebroken sneeuwdek >=1 cm

999 sneeuwhopen

751,20141223, 0, 0,

751,20141224, 10, 0,

751,20141225, 37, 0,

751,20141226, 70, 0,

751,20141227, 350, 998,

751,20141228, 46, 998,

751,20141229, 18, 0,

751,20141230, 4, 0,

Data from the ‘Neerslagstation’ Vrouwepolder

AutomatingAppliance Detection

• Appliance What???

• KNIME server

Appliance Type Detection

?

?

AutomationPlatform Architecture

AutomationAppliance Detection

Time for KNIME

KNIME tips

• loops

• multiple axes -> manipulate data

• email reports

• wrapped nodes + quickform

• read variables from file

• knime URI

KNIME tipswrapped nodes + quickform

KNIME tipsread variables from file

KNIME URIs

knime://knime.workflow/../../environment.variables

Thank you