Building a semantic-based decision support system to optimize the energy use in public buildings: the OPTIMUS project

A Semantic Decision Support System to optimize the energy use of public buildings

Álvaro Sicilia, Gonçal Costa, Leandro MadrazoARC Engineering and Architecture La SalleRamon Llull University, Barcelona, SpainVincenzo Corrado, Alice GorrinoDepartment of EnergyPolitecnico di Torino, Torino, ItalyFulvio CornoDepartment of Control and Computer EngineeringPolitecnico di Torino, Torino, Italy

Gonçal Costa – ARC, La SalleOctober 27th -29th, 2015 Eindhoven, The Netherlands

Outline

1. “Smart cities” paradigm for decision making

2. Decision support systems overview

3. The OPTIMUS DSS

4. The Semantic Framework

5. Conclusions / Collaboration


The “smart cities” approach can help to improve the citizens’ quality of life in accordance with the objectives set by sustainable energy policies of the European Union with the target of reducing by 20% the CO2 emissions by 2020

Smart cities rely on the availability of data

although there are more and more energy and other related data sets available, it is necessary to integrate them in order to provide the various key actors the information they need to make well-informed decisions

it is not enough to have access to the data

it is necessary to integrate data from different domains in order to understand the interrelationships between the various areas –energy, economics, social– that are involved in the reduction of carbon emissions in cities



these technologies can be applied, mainly, to support data integration processes and to overcome the interoperability barriers between the data generated by the different users and by the applications

There are well-known issues:• Type of accessing• Syntax of the content• Data format

application of Semantic Web technologies can help to overcome some of the difficulties which are intrinsic to the development of decision support systems which rely on distributed and heterogeneous data


Data

User

Data

Data

User

User

User

User

Data Application

Systems


these technologies can be applied, mainly, to support data integration processes and to overcome the interoperability barriers between the data generated by the different users and by the applications

Data

User

Data

Data

User

User

User

User

Data Application

SWSolutions for data integration with explicit semantics can ensure that the meaning of data can be unambiguously understood by both humans and systems

There are well-known issues:• Type of accessing• Syntax of the content• Data format

……….OWL, RDF, SPARQL

application of Semantic Web technologies can help to overcome some of the difficulties which are intrinsic to the development of decision support systems which rely on distributed and heterogeneous data


Systems


Outline



3. The OPTIMUS DSS





Classification of DSSs [1]:- Communication-driven DSS: use a set of parameters provided by decision makers to

assist them in analysing its particular problem. - Data-driven DSS: are based on analysing time-series data as well as external

and real-time data.- Document-driven DSS: are focused on providing search functionalities to help

managers to find documents- Knowledge-driven DSS: are based on the knowledge extraction from a particular

domain to be analysed using data mining methods, - Model-driven DSS: operate on a model of reality rather than on data intensive model.

Use of Semantic Web Technologies for decision support:- Semantic web technologies can be applied in DSS developments using ontologies and

rules as a means to provide intelligent support to decision-making [2]. - They can be used to support data integration processes, and to overcome the

interoperability barriers through standardized formats.

[1] Power, D. J. (2002). Decision Support Systems: Concepts and Resources for Managers. Westport, CT: Greenwood/Quorum[2] Blomqvist, E. (2012). The Use of Semantic Web Technologies for Decision Support - A Survey, In: Semantic Web Journal, 5(3): 177-201, IOS Press.



Decision support systems in energy efficiency:

- At present, analysis techniques of energy efficiency of buildings used for decision support are limited to a very few data sources.

- The most commonly used data sources are those provided by a building management system (e.g., energy consumption, temperature, humidity and CO2), while other such as weather forecasting, social media and occupancy in most cases are not considered [3].

- However, and as a result of the increasing demand to satisfy the current legislative framework; for example, to meet the European directives in terms of energy efficiency in buildings; new paradigms and systems are emerging with the aim of achieving a more comprehensive view of the energy performance of the building

- Examples of research projects: EEPOS (2015), EnRiMa (2013), SEMANCO (2013), SEMERGY (2014), KnoholEM (2014).

[3] Corry, E., Coakley, D., O'Donnell, J., Pauwels, P. and Keane, M. (2013). The role of Linked Data and the Semantic Web in Building Operation. Proceedings of the 13th annual International Conference for Enhanced Building Operations (ICEBO). Montréal, Canada.


EECITIES: Service platform to support planning of energy efficient cities [4]

An energy analysis service provider that supports planners, energy consultants, and policy makers to make informed decisions related to improving the energy efficiency of urban areas.

Integration of dispersed energy related data from multiple sources, including Cadastre, census, socio-economic, building typologies (u-values, windows properties, systems)

[4] Madrazo, L., Nemirovski, G., Sicilia. A. (2013). Shared Vocabularies to Support the Creation of Energy Urban Systems Models. In Proceedings 4th Workshop organised by the EEB Data Models Community ICT for Sustainable Places, Nice, France, September, 2013.

from: http://www.eecities.com

OWL 2 QL profileSUMO Ontology, R2RML mappings, Query federation,

http://www.eecities.com/

Services:- Assessing the current energy

performance of buildings in towns and cities.

- Identifying priority areas and buildings for energy efficiency interventions.

- Evaluating the impact of refurbishing buildings at the urban level.

- …



Outline



3. The OPTIMUS DSS




Semantic framework

Weather forecasting

De-centralized sensor-based

Social media/ mining

Energy prices

RES production

Data is captured from the buildings and their context. Semantic framework integrates the different data sources using semantic web technologies.

DATA CAPTURING MODULES

3. The OPTIMUS DSS


Semantic framework

Weather forecastin

g



Energy prices

RES production

PREDICTION MODELS

Historical data

Predicted data

Prediction models use historical data to forecast the building behaviour for the following 7 days


3. The OPTIMUS DSS


Semantic framework

Weather forecastin

g



Energy prices

RES production

PREDICTION MODELS

INFERENCE RULES

Historical data

Predicted data Inference rules use the predicted and monitored data to suggest short-term actions to the final user

Monitored data


Relations between input data (real time and predicted data, and static user inputs) for suggesting an action plan

3. The OPTIMUS DSS


Semantic framework

Weather forecastin

g



Energy prices

RES production

PREDICTION MODELS

INFERENCE RULES

Historical data

Predicted data

Monitored data


DSS interfaces display the monitored data, forecasted data, and short-term plans in order to support users’ decisions

DSS INTERFACE


3. The OPTIMUS DSS


Semantic framework

Weather forecastin

g



Energy prices

RES production

PREDICTION MODELS

INFERENCE RULES

Historical data

Predicted data

Monitored data


Sant Cugat

Savona

Zaanstad

The results of the implementation of the actions in each pilot city will modify the data sources

DEMONSTRATIONDSS INTERFACE


3. The OPTIMUS DSS


The OPTIMUS DSS optimizes the use of energy, suggesting ACTION PLANS:

• boost time of the heating/cooling system taking into account the forecasting of the outdoor/indoor air temperature and the occupancy of the building.

• selling/self-consuming of electricity produced by a PV system considering different scenarios of energy market and strategies (green, finance, peak).

• adjustment of the temperature set-point, taking into consideration thermal comfort parameters (e.g., Predicted Mean Vote index) using occupants’ inputs gathered with a mobile app.

3. The OPTIMUS DSS


The innovative approach of the OPTIMUS DSS is based on the combination of:

• The use of multidisciplinary data sources, including:• Weather forecasting• Social media • Occupancy

• The semantic modelling of data using Semantic Web technologies

• The integration of data for energy optimization

3. The OPTIMUS DSS


Outline



3. The OPTIMUS DSS





Social media

Sources

Data capturing modules

Virtuoso Server

Weather data De-centralized data Energy prices Energy

production data

Semantic Framework

RapidAnalytics: Prediction models

Semantic Service

Ztreamy Server

PHP Services: Inference rules

MariaDB

Data portal. Elda: the linked-data API in Java

End-user web environment

Management environment

DSS engine

Developed within OPTIMUS project External source used by OPTIMUS

Sources Sources Sources Sources

DSS environments

R scripts

Internal architecture of the OPTIMUS DSS


Social media

Sources

WP2 Data capturing modules

Virtuoso Server

Weather data De-centralized data Energy prices Energy

production data

RapidAnalytics: Prediction models

Semantic Service

Ztreamy Server

PHP Services: Inference rules

MariaDB

Data portal. Elda: the linked-data API in Java

End-user web environment

Management environment

DSS engine

Developed within OPTIMUS project External source used by OPTIMUS

Sources Sources Sources Sources

DSS environments

R scripts


Semantic Framework



We have devised a semantic framework which is composed of:

1. A shared conceptualization of the urban and building domain including monitoring devices, formally implemented as the OPTIMUS ontology coded in OWL.

2. A semantic integration process for capturing and modelling data sources from different domains.

- Two RDF templates used by the data capturing modules for modelling real-time information items, according to the OPTIMUS ontology.

- A publish-and-subscribe system as a communication infrastructure between the data capturing modules and the DSS implemented with the Ztreamy system and a semantic service which processes the data with the purpose of contextualizing them.



Sensors(based on SSN ontology)

OPTIMUS ontology

Building & systems features(based on Semanco ontology)

1. The OPTIMUS ontology reuse two existing ontologies:

- Static data (Building and systems features) has been modelled with the SEMANCO ontology http://semanco-tools.eu/ontology-releases/eu/semanco/ontology/SEMANCO/SEMANCO.owl

- Dynamic data (sensoring) has been modelled with the Semantic Sensor Network (SSN) ontology

http://purl.oclc.org/NET/ssnx/ssn

http://semanco-tools.eu/ontology-releases/eu/semanco/ontology/SEMANCO/SEMANCO.owl







Ontologies

ssn:Sensor

ssn:SensingDevice

ssn:Observation

optimus:SunnyPortal_EnergyProduction

semanco:Solar_Irradiationssn:FeatureOfInterest

ssn:Property

ssn:System

subClassOf

subClassOf

ssn:hasSubSystem

ssn:observes

ssn:observes

subClassOf

ssn:hasProperty

subClassOf

ssn:observedBy

subClassOf

ssn:featureOfInterest

ssn:observedProperty

semanco:PVSystem_Peak_Power

optimus:SunnyPortal_SolarRadiation

subClassOf

ssn:SensorOutput

ssn:observationResult

ssn:hasValue

time:Instant

ssn:observationResultTime

time:inXSDDateTime

literal

ssn:Platform

ssn:Deploymentssn:deployedOnPlatform

ssn:hasDeployment

sumo:located

sumo:Building

sumo:Room

Semanco:Space_Heating_System

Semanco:Ventilation_System

…

literal

subClassOf

optimus:Solar_IrradiationSensorOutput

optimus:PVSystem_Peak_PowetSensorOutput

subClassOf

ssn:onPlatform

optimus:SunnyPortal

subClassOf

subClassOfssn:observes

optimus:Solar_IrradiationFeature

subClassOf

optimus:PVSystem_Peak_PowerFeature

ssn:hasProperty

Static part of the ontology Building and System features

Semantic Sensor Network

OPTIMUS

SEMANCO



Triple store (Virtuoso Server)

Semantic Framework

Weather data

De-centralized data

Social media

Ztreamy

Server

Energy prices

Energy production

Semantic

Service

Data capturing modulesSourcesOPTIMUS DSS

2. a semantic integration process for capturing and modelling data sources from different domains.

RAW DATA

RDF DATA: RAW DATA +

MEANING

RDF DATA +

CONTEXT

INTEGRATED DATA




Semantic Framework

Weather data

De-centralized data

Social media

Ztreamy

Server

Energy prices

Energy production

Semantic

Service


1. Data translatio

n

RAW DATA




Semantic Framework

Weather data

De-centralized data

Social media

Ztreamy

Server

Energy prices

Energy production

Semantic

Service


1. Data translatio

n

2. Data communicati

on

RAW DATA


MEANING

publishers




Semantic Framework

Weather data

De-centralized data

Social media

Ztreamy

Server

Energy prices

Energy production

Semantic

Service


1. Data translatio

n

2. Data communicati

on

RAW DATA


MEANING

RDF DATA +

CONTEXT

publishers

Subscriber

3. Data contextualiza

tion




Semantic Framework

Weather data

De-centralized data

Social media

Ztreamy

Server

Energy prices

Energy production

Semantic

Service

RAW DATA


MEANING

RDF DATA +

CONTEXT

INTEGRATED DATA


1. Data translatio

n

2. Data communicati

on

3. Data contextualiza

tion4. Data storage

publishers

Subscriber



Ontologies

ssn:Sensor

ssn:SensingDevice

ssn:Observation



ssn:Property

ssn:System

subClassOf

subClassOf

ssn:hasSubSystem

ssn:observes

ssn:observes

subClassOf

ssn:hasProperty

subClassOf

ssn:observedBy

subClassOf





subClassOf

ssn:SensorOutput


ssn:hasValue

time:Instant


time:inXSDDateTime

literal

ssn:Platform


ssn:hasDeployment

sumo:located

sumo:Building

sumo:Room



…

literal

subClassOf



subClassOf

ssn:onPlatform

optimus:SunnyPortal

subClassOf



subClassOf


ssn:hasProperty



OPTIMUS

SEMANCO

RAW DATA



Ontologies

ssn:Sensor

ssn:SensingDevice

ssn:Observation



ssn:Property

ssn:System

subClassOf

subClassOf

ssn:hasSubSystem

ssn:observes

ssn:observes

subClassOf

ssn:hasProperty

subClassOf

ssn:observedBy

subClassOf





subClassOf

ssn:SensorOutput


ssn:hasValue

time:Instant


time:inXSDDateTime

literal

ssn:Platform


ssn:hasDeployment

sumo:located

sumo:Building

sumo:Room



…

literal

subClassOf



subClassOf

ssn:onPlatform

optimus:SunnyPortal

subClassOf



subClassOf


ssn:hasProperty



OPTIMUS

SEMANCO

RDF template for data capturing modules

RDF DATA: RDF DATA + MEANING

RAW DATA



Ontologies

ssn:Sensor

ssn:SensingDevice

ssn:Observation



ssn:Property

ssn:System

subClassOf

subClassOf

ssn:hasSubSystem

ssn:observes

ssn:observes

subClassOf

ssn:hasProperty

subClassOf

ssn:observedBy

subClassOf





subClassOf

ssn:SensorOutput


ssn:hasValue

time:Instant


time:inXSDDateTime

literal

ssn:Platform


ssn:hasDeployment

sumo:located

sumo:Building

sumo:Room



…

literal

subClassOf



subClassOf

ssn:onPlatform

optimus:SunnyPortal

subClassOf



subClassOf


ssn:hasProperty



OPTIMUS

SEMANCO

RDF data from modules + context data

RAW DATA

RDF DATA: RDF DATA + MEANING RDF DATA +

CONTEXT








Outline



3. The OPTIMUS DSS




5. Conclusions

The OPTIMUS DSS represents an innovation with respect to the existing systems in so far it is able to interlink five types of heterogeneous and dynamic data sources in order to suggest short-term actions plans that enable public authorities to reduce energy consumption in public buildings

In the semantic framework proposed in OPTIMUS, the use of RDF templates have been an important mechanism to provide a standardized way of integrating heterogeneous data sources. However, a monitoring process is required from the beginning to ensure that developers of capture modules are following the specification of the template.

Requirements capturing is not a one-time process. It needs to be continuously validated with end-users and domain experts


Gonçal Costa

ARC Engineering & ArchitectureLa Salle, Ramon Llull universityQuatre Camins, 2 08022, Barcelona, SPAINTel. +34 93 290 24 49 Fax +34 93 290 24 20E-mail: [email protected]

Technology

Building a semantic-based decision support system to optimize the energy use in public buildings: the OPTIMUS project