monet, an iot energy management platform based on mongodb

© Siemens AG 2015 siemens.com/EUW

Monet An IoT Energy Management Platform based on MongoDB

MongoDB Event | Milano, 14 January 2016

© Siemens AG 2015

2015-11-04 Maurizio Bigoloni / RC IT EM DG SWS

Energy Management Division\ Key numbers

~ €11 bn Revenue

~ 53,000 People

~ 100 Sites

~ €350 m R&D Investments

Locations Energy Management

© Siemens AG 2015


Energy Management Division\ Portfolio

Serv

ices

& S

ecur

ity

Software/ITGrid control – big data analytics – grid application

Dig

italiz

atio

nA

utom

atio

n

Communication, automation, protection, field devices

Ele

ctrif

icat

ion

Electrification SolutionsHigh-voltage direct current (HVDC) transmission – grid access – FACTS – air-insulated/gas-insulated substations – power systems solutions – microgrids / nanogrids

Products & SystemsHigh-voltage switchgear and systems – power transformers – medium-voltage switchgears – distribution transformers – low-voltage switchboards and circuit breakers

Large powergeneration

TSOs1 Oil and gas Industria Infrastructures / construction

DSOs2 and municipalities

Distributedgeneration

1 Transmission system operators 2 Distribution system operators

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Digitalization \ Enables customers to turn threats into opportunities

Digital services Vertical software

Digitally enhanced electrification and automation

Customers benefit• Increased productivity and flexibility• Shorter time to market• Improved uptime and lifetime

Challenges Digitalization delivers answers

ALERT!

Balancing

Peak avoidance

Resilience

Business models

CO2 and cost avoidance

Loss prevention

Distributed optimization

Customer focus

© Siemens AG 2015


IOT based Energy Management System \ Keypoints

• Micro-Services Architecture

• Scalable/Reliable• Html5 web applications• Software as a Service

• Standard protocols (MQTT, AMQP)

• General-purpose data acquisition

• Real-time data aggregation

• Real-time data analysis

CloudInternet of Things Analytics

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Internet of Things & Energy Management\ Innovative Services \ Energy Efficiency

Real-time data acquisition and data aggregation are enabler for energy efficiency advanced services. Internet of Things paradigm allows integrating end-user devices to get preferences and behavior information.

Energy Rules based on:

• Real-time measures

• Load & Generation Profile/ Forecast

• User Preferences / Environmental data

Energy Rules actions:

• Load Control/Shifting

• Storage Control

• Comfort variables set points

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Internet of Things & Energy Management\ Innovative Services \ Demand Response

Integrating energy stakeholder systems (TSO, DSO, Energy Vendor) with end-user systems and devices allows implementation of innovative services toward Demand Response:

• Energy Vendor / Dynamic Price

• DSO / Grid Emergency

• DSO / Peak Shaving

• DSO / Electric Mobility integration

• Aggregation of Consumer/Producer

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Milan Expo 2015\ A unique opportunity

Expo 2015 a unique opportunity to build a Smart City from green field:

• 1’100’000 m2 Area

• 75MW Planned Power

• 145 Countries

• 53 Self-Built Pavilions

• Fiber optics backbone

• Wi-Fi infrastructure (2’700 AP)

Siemens strategic partner of Enel for the Smart Grid technology at EXPO Milano 2015

© Siemens AG 2015


Milan Expo 2015\ Project details

SMARTMETERING

GRID TECHNOLOGIES

ELECTRIC MOBILITY

SMART LIGHTING

OPERATION CENTERS

PV PLANTSSMART SUBSTATIONS

ELECTRO-MOBILITY

50

8500ARCHILEDE OUTDOOR SOLUTIONS

SMART BUILDING

100 200 5

BUILDING MANAGEMENTROOM

AUTOMATION

ENERGYSTORAGE

1

30 300

2

© Siemens AG 2015


Milan Expo 2015\ Energy Management System

Online since May1st

EMS backendREST API

EMS web applications

MQTT / AMQP

Energy Monitoring / Reporting Energy Profiling / Forecast Energy Efficiency /

Demand Response

Enterprise Applications

1325

6

1. Grid Substations: P measures every 5’

2. GME Meter on each MV/LV transformer – via GSM every 15’

3. Enel Meter on each LV line – via Wi-Fi every 5’

4. Multi-meter on each controllable load; temperature & lighting sensors – via Wi-Fi every 5’

5. Charging Units – via GSM every 15’

6. Public Lighting Panles – every 60’

4

© Siemens AG 2015


IOT based Energy Management System \ IT architecture

EMS backend

Local Control

REST API

EMS web applications

MQTT / AMQP

Energy Monitoring / Reporting

Energy Profiling / Forecast

Energy Efficiency / Demand Response

Enterprise Applications

Smart Home

Personal

Devices

Distribution Network SCADA

Public Lighting

Building Mngt

System

Electric Mobility

PlantSCADA

Smart Meter

MeterDataMngt

Fiel

dP

rem

ise

Clo

ud

MongoDB

© Siemens AG 2015


IOT based Energy Management System \ Why a NO SQL database?

During the design phase the database selection was a key step: SQL, NO-SQL, or both?

At the end the choice was to go to NO-SQL only selecting MongoDB:

• General-purpose data acquisition layer schema-less databases best option for modeling different kind of objects

• Data acquisition layer requires a large number of WRITE operations NO-SQL more promising for keeping constants the WRITE performances

• Design for Cloud MongoDB scalability fits well with Cloud

• Given the full JavaScript application stack (node.js + html5) a JSON based document database as MongoDB resulted to be the natural choices for the entire system

© Siemens AG 2015


IOT based Energy Management System \ Data acquisition

Data acquisition layer in Monet is based on MQTT protocol (www.mqqt.org). MQTT is a standard lightweight protocol adopting the publish/subscribe paradigm.

MQTT is based on the topic concept; a topic is a stream of data coming from or going to a particular I/O of a particular device. So it can contain data from the field but also commands.

The Feed Broker is the Monet module that collects the data. It contains a MQTT broker called Mosca. When a message arrives to the broker, the payload is stored as raw data in MongoDB.

http://www.mqqt.org/

© Siemens AG 2015


IOT based Energy Management System \ Real-Time data aggregation and data analysis

Raw data coming from field devices needs to be analyzed; for any field variable there is the possibility to calculate Trends:

• Curve at fixed precision from raw data (avg, min, max, sum)

• Daily, Weekly, Monthly, Yearly trends calculated at different precisions

Trends curves related to energy variables (energy, power) are aggregated by different hierarchies:

• Geographical

• Electrical

• Technical

• By usage, scenario, mode

© Siemens AG 2015


IOT based Energy Management System \ MongoDB aggregation framework

The Aggregation Framework allows easy and efficient aggregation on raw data. Here an example on how we store raw data in the datapoints collection.

{ feedId: “ABCD” date: 16/01/2016 values: [ {v: 10, ts: 1452902400000}, {v: 20, ts: 1452903000000}, {v: 30, ts: 1452903600000} ]}

{ feedId: “ABCD”, v: 10, ts: 1452902400000}

{ feedId: “ABCD”, v: 20, ts: 1452903000000}

{ feedId: “ABCD”, v: 30, ts: 1452903600000}}

VS

Data pre-aggregation,1 document per day, has several advantages: Many fewer documents: 1 per day vs 1 per

datapoint (hundreds of them!) Index space largely reduced, thus occupying less

disk and RAM Less I/O operations working on just a single

document All of this leads to overall better performance

© Siemens AG 2015



Here the simple instruction to aggregate raw data into

db.datapoints.aggregate([ { $match: { “feedId”: “ABCD”, “date”: { “$gte”: yesterday }}}, { $unwind: “$values” }, { $group: {_id : {'$subtract': [{'$divide': ["$values.ts", 3600000]}, {'$mod': ["$values.ts", 3600000]}]}},

date: date,max: {$max: "$values.v"},min: {$min: "$values.v"},avg: {$avg: "$values.v"},sum: {$sum: "$values.v"}

}]);

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{ feedId: “ABCD” date: 12/01/2016 values: [{ v: 10, ts: 1452556800000 }]}


{ feedId: “ABCD” date: 16/01/2016 values: [ {v: 10, ts: 1452902400000} ]}{ feedId: “ABCD” date: 16/01/2016 values: [ {v: 20, ts: 1452903000000} ]}{ feedId: “ABCD” date: 16/01/2016 values: [ {v: 30, ts: 1452903000000} ]}

{ _id: 403584, date: 16/01/2016 max: 30, min: 10, avg: 20, sum: 60}


match unwind group

Input Result

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IOT based Energy Management System \ Some numbers

6 months system running at EXPO Milano 2015

120.000.000 raw datapoints collected

45 GB raw datapoints collection

17 GB trend curves collection

5 GB energy aggregation curves collection

© Siemens AG 2015


Internet of Things & Energy Management \ Conclusion

• Connected Things• Home Devices• Personal Devices

• Electric Grid• Communication Network • Distributed Generation

• Innovative Services• Real-Time Data Analysis• Efficiency• Demand Response• Aggregation

Internet of ThingsSmart Grid Digital Grid+ =

© Siemens AG 2015


Maurizio BigoloniHead of OperationRC IT EM DG SWS

Via Vipiteno, 420128 Milano

Phone: +39 02 243 23335Mobile: +39 334 8888744

E-mail:[email protected]

Energy of Things\ Contact page

siemens.com/EUW

mailto:[email protected]

monet, an iot energy management platform based on mongodb

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