=Paper=
{{Paper
|id=Vol-1401/paper-09
|storemode=property
|title=Demonstration: Web-based Visualisation and Monitoring of Smart Meters using CQELS
|pdfUrl=https://ceur-ws.org/Vol-1401/paper-09.pdf
|volume=Vol-1401
|dblpUrl=https://dblp.org/rec/conf/semweb/KolchinMA14
}}
==Demonstration: Web-based Visualisation and Monitoring of Smart Meters using CQELS==
https://ceur-ws.org/Vol-1401/paper-09.pdf
Demonstration: Web-based Visualisation and
Monitoring of Smart Meters using CQELS
Maxim Kolchin, Dmitry Mouromtsev, Sergey Arustamov
ITMO University
Saint-Petersburg, Russia
kolchinmax@niuitmo.ru, {d.muromtsev,sergey.arustamov}@gmail.com
Abstract. In this demonstration paper we describe a web application
that allows visualisation of current and historical readings of smart me-
ters and monitoring their current states using CQELS for an integration
of static and streaming RDF sources.
Keywords: Ontologies, RDF stream processing, Smart Meter, Data In-
tegration, Visualisation
1 Introduction
Semantic Web technologies have already proved [1, 2] that they may become a
tool for solutions aiming to solve issues with syntactic and semantic integration of
data in heterogeneous formats, represented by different models. RDF and OWL
provide a common model and vocabulary for such type of data with SPARQL to
query data sources and Reasoning to infer ”new information” using first-order
predicate logic and inference rules. Moreover, RDF stream processing technolo-
gies allow combining background knowledge and streaming data together.
These sort of ideas may be applied with Smart Meter data where integration
of static and streaming data is important to monitor effectively the state of
the meters and the whole network and also respond immediately to emergency
situations and unpredictable events.
In our case we have three types of smart electric meters and totally 55 in-
stances that are able to transmit their readings each 5 minutes to a server. Our
goal is twofold: (a) to be able to see the current reading in real-time and pre-
vious ones within a period with charts and (b) more importantly to automate
identification of emergency situations, i.e too high voltage value in a line.
In this demo we show how we have tuned the web application to access
streaming data coming from smart meters the same way it access a SPARQL
endpoint.
The source code of the web application can be found on Github1 .
1
The source code of the application, URL: https://github.com/ailabitmo/daafse
2 Architecture & Implementation
The architecture of the application is shown on Fig 1. The streaming data (the
current readings) coming from smart meters is collected and transformed by
RDF stream publisher to RDF streams that are in their turn published through
the Message Bus implementing AMQP2 protocol. Each RDF stream has an URI
represented by AMQP URI3 schema, i.e amqp://example.com:10000/vhost?
exchangeName=exchange&routingKey=mercury230_13534128. The static data
in SPARQL endpoint presents information about smart meters, like a type, serial
number, installation location, precision, operating range and etc. Integration
of static and streaming sources is performed through CQELS engine and an
extension of SPARQL language supporting RDF streams. CQELS engine and
the query language are presented in the paper [3].
Fig. 1. System architecture: The App, Smart Meters, SPARQL endpoint and RDF
streams
The Web App visualises the readings in a form of charts in real-time and
allows to register a CQELS query that goes off alerts in case if there are some
issues in functioning of smart meter or the network, i.e. there is a way to register
a query the signals of a too high voltage value (≥ 220V + 10%) measured at least
by one of known smart meters.
The result of the query is a bag of triples representing an alert that is pub-
lished to an RDF stream. See an example query and alert in Section 2.2.
2
Advanced Message Queuing Protocol (AMQP), URL: http://en.wikipedia.org/
wiki/Advanced_Message_Queuing_Protocol
3
AMQP URI spec, URL: https://www.rabbitmq.com/uri-spec.html
2.1 Ontologies
Several domain ontologies are used to represent the readings, smart meters, and
alerts: Semantic Sensor Network (SSN)4 ontology, the Electric Meters (EM)5
ontology that is an extension of SSN ontology developed for this application and
The DOLCE+DnS Ultralite ontology6 . On Fig. 2 you can see a smart meter and
an observation represented with these ontologies.
Fig. 2. Smart Meter and Observation
The EM ontology extends SSN ontology with several classes and properties:
– em:ElectricityFeature is a subclass of ssn:FeatureofInterest,
– em:PolyphaseVoltageObservation is a subclass of ssn:Observation,
– em:hasStream object property representing an RDF stream URI,
– and other classes and properties.
2.2 An example query
Below we show an example of query firing an alert each time when voltage value
in any of electric phases measured by one of smart meters is ≥ 220V + 10%. All
alerts are sent to a predefined RDF stream and consumed by the Web App.
4
Semantic Sensor Network ontology, URL: http://purl.oclc.org/NET/ssnx/ssn#
5
The Electric Meters ontology, URL: http://purl.org/NET/ssnext/
electricmeters#
6
DOLCE+DnS Ultralite ontology, URL: http://www.loa-cnr.it/ontologies/DUL.
owl#
CONSTRUCT {
?alert a :TooHighVoltageValue ;
dul:hasEventDate ?time ;
dul:involvesAgent ?meter .
}
FROM NAMED
WHERE {
GRAPH { ?meter em:hasStream ?stream }
STREAM ?stream [NOW] {
?observation a em:PolyphaseVoltageObservation ;
ssn:observationResultTime ?time .
?output a em:PolyphaseVoltageSensorOutput ;
ssn:isProducedBy ?meter ;
ssn:hasValue ?value .
?value em:hasQuantityValue ?qvalue .
}
FILTER(?qvalue >= (220 + 220*0.1))
BIND (IRI(CONCAT(STR(?meter), ’/alerts/’, STR(?time))) AS ?alert)
}
RDF stream URIs of smart meters are queried from http: /ex. com/ SmartMeters/
graph in http: // ex. com/ sparql SPARQL endpoint. In a more complicated
scenario the highest possible voltage value for different smart meters may vary
and instead of creating a separate alert for each type of meters this value can be
queried from a SPARQL endpoint.
In this example an alert is an instance of :TooHighVoltage class that is a
subclass of dul:Event class. Below a set of triples describing the alert:
:TooHighVoltage owl:subClassOf dul:Event ;
rdfs:comment ...@en ;
rdfs:label ...@en ;
dul:isClassifiedBy :Warning .
Acknowledgments. This work has been partially financially supported by the
Government of Russian Federation, Grant #074-U01.
References
1. Tallevi-Diotallevi S., Kotoulas S., Foschini L., Lcu F., Corradi A.: Real-Time Urban
Monitoring in Dublin Using Semantic and Stream Technologies. In: The Semantic
Web–ISWC 2013, pp. 178–194. Springer Berlin Heidelberg (2013)
2. Balduini M., Della Valle E., Dell‘Aglio D., Tsytsarau M., Palpanas T., Confalonieri
C.: Social listening of city scale events using the streaming linked data framework.
In: The Semantic Web–ISWC 2013, pp. 1–16. Springer Berlin Heidelberg, (2013)
3. Le-Phuoc D., Dao-Tran M., Xavier Parreira J., Hauswirth M.: A Native and Adap-
tive Approach for Unified Processing of Linked Streams and Linked Data. In: The
Semantic Web ISWC 2011, pp. 370–388. Springer Berlin Heidelberg, (2011)