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A Semantic Decision Support System to
optimize the energy use of public buildings
Álvaro Sicilia, Gonçal Costa, Leandro Ma...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Outline
1. “Smart cities” paradigm for de...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
The “smart cities” approach can help to i...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
these technologies can be applied, mainly...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
these technologies can be applied, mainly...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Outline
1. “Smart cities” paradigm for de...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
2. Decision support systems overview
Clas...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
2. Decision support systems overview
Deci...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
EECITIES: Service platform to support pla...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Outline
1. “Smart cities” paradigm for de...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Semantic framework
Weather
forecasting
De...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Semantic framework
Weather
forecastin
g
D...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Semantic framework
Weather
forecastin
g
D...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Semantic framework
Weather
forecastin
g
D...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Semantic framework
Weather
forecastin
g
D...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
The OPTIMUS DSS optimizes the use of ener...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
The innovative approach of the OPTIMUS DS...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Outline
1. “Smart cities” paradigm for de...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Social media
So...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Social media
Sources
WP2 Data capturing
m...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
We have devised...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Sensors
(based ...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Ontologies
ssn:...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Triple store
(V...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Triple store
(V...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Triple store
(V...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Triple store
(V...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Triple store
(V...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Ontologies
ssn:...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Ontologies
ssn:...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Ontologies
ssn:...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
4. The Semantic Framework
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Outline
1. “Smart cities” paradigm for de...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
5. Conclusions
The OPTIMUS DSS represents...
Gonçal Costa – ARC, La Salle
October 27th -29th, 2015 Eindhoven, The Netherlands
Gonçal Costa
ARC Engineering & Architectu...
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Building a semantic-based decision support system to optimize the energy use in public buildings: the OPTIMUS project

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The reduction of carbon emissions in cities is a systemic problem which involves multiple scales and domains and the collaboration of experts from various fields. The smart cities approach can contribute to improve the energy efficiency of urban areas provided that there is reliable data –from the different domains concerned with carbon emission reduction– to assess their energy performance and to make decisions to improve it. In the SEMANCO project, we applied Semantic Web technologies to solve the interoperability among data, systems, tools, and users in applications cases dealing with carbon emission reduction in urban areas. In the OPTIMUS project, the tools and methods developed in SEMANCO are being further enhanced and applied to the development of a decision support system (DSS) to help local administrations to optimize the energy use of public buildings.

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

  1. 1. A Semantic Decision Support System to optimize the energy use of public buildings Álvaro Sicilia, Gonçal Costa, Leandro Madrazo ARC Engineering and Architecture La Salle Ramon Llull University, Barcelona, Spain Vincenzo Corrado, Alice Gorrino Department of Energy Politecnico di Torino, Torino, Italy Fulvio Corno Department of Control and Computer Engineering Politecnico di Torino, Torino, Italy
  2. 2. Gonçal Costa – ARC, La Salle October 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
  3. 3. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 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 1. “Smart cities” paradigm for decision making
  4. 4. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 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 1. “Smart cities” paradigm for decision making Data User Data Data User User User User Data Application Systems
  5. 5. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 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 SW Solutions 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 1. “Smart cities” paradigm for decision making Systems
  6. 6. Gonçal Costa – ARC, La Salle October 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
  7. 7. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 2. Decision support systems overview 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.
  8. 8. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 2. Decision support systems overview 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.
  9. 9. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 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 profile SUMO 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. - … 2. Decision support systems overview
  10. 10. Gonçal Costa – ARC, La Salle October 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
  11. 11. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 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
  12. 12. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands Semantic framework Weather forecastin g De-centralized sensor-based Social media/ mining 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 DATA CAPTURING MODULES 3. The OPTIMUS DSS
  13. 13. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands Semantic framework Weather forecastin g De-centralized sensor-based Social media/ mining 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 DATA CAPTURING MODULES Relations between input data (real time and predicted data, and static user inputs) for suggesting an action plan 3. The OPTIMUS DSS
  14. 14. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands Semantic framework Weather forecastin g De-centralized sensor-based Social media/ mining Energy prices RES production PREDICTION MODELS INFERENCE RULES Historical data Predicted data Monitored data DATA CAPTURING MODULES DSS interfaces display the monitored data, forecasted data, and short-term plans in order to support users’ decisions DSS INTERFACE Relations between input data (real time and predicted data, and static user inputs) for suggesting an action plan 3. The OPTIMUS DSS
  15. 15. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands Semantic framework Weather forecastin g De-centralized sensor-based Social media/ mining Energy prices RES production PREDICTION MODELS INFERENCE RULES Historical data Predicted data Monitored data DATA CAPTURING MODULES Sant Cugat Savona Zaanstad The results of the implementation of the actions in each pilot city will modify the data sources DEMONSTRATIONDSS INTERFACE Relations between input data (real time and predicted data, and static user inputs) for suggesting an action plan 3. The OPTIMUS DSS
  16. 16. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 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
  17. 17. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 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
  18. 18. Gonçal Costa – ARC, La Salle October 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
  19. 19. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework 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
  20. 20. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 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 4. The Semantic Framework Semantic Framework
  21. 21. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The 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.
  22. 22. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework 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
  23. 23. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework 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:Deployment ssn: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 subClassOf ssn: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
  24. 24. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework Triple store (Virtuoso Server) Semantic Framework Weather data De-centralized data Social media Ztreamy Server Energy prices Energy production Semantic Service Data capturing modulesSources OPTIMUS 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
  25. 25. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework Triple store (Virtuoso Server) Semantic Framework Weather data De-centralized data Social media Ztreamy Server Energy prices Energy production Semantic Service Data capturing modulesSources OPTIMUS DSS 1. Data translation RAW DATA
  26. 26. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework Triple store (Virtuoso Server) Semantic Framework Weather data De-centralized data Social media Ztreamy Server Energy prices Energy production Semantic Service Data capturing modulesSources OPTIMUS DSS 1. Data translation 2. Data communication RAW DATA RDF DATA: RAW DATA + MEANING publishers
  27. 27. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework Triple store (Virtuoso Server) Semantic Framework Weather data De-centralized data Social media Ztreamy Server Energy prices Energy production Semantic Service Data capturing modulesSources OPTIMUS DSS 1. Data translation 2. Data communication RAW DATA RDF DATA: RAW DATA + MEANING RDF DATA + CONTEXT publishers Subscriber 3. Data contextualization
  28. 28. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework Triple store (Virtuoso Server) Semantic Framework Weather data De-centralized data Social media Ztreamy Server Energy prices Energy production Semantic Service RAW DATA RDF DATA: RAW DATA + MEANING RDF DATA + CONTEXT INTEGRATED DATA Data capturing modulesSources OPTIMUS DSS 1. Data translation 2. Data communication 3. Data contextualization 4. Data storage publishers Subscriber
  29. 29. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework 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:Deployment ssn: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 subClassOf ssn: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 RAW DATA
  30. 30. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework 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:Deployment ssn: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 subClassOf ssn: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 RDF template for data capturing modules RDF DATA: RDF DATA + MEANING RAW DATA
  31. 31. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework 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:Deployment ssn: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 subClassOf ssn: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 RDF data from modules + context data RAW DATA RDF DATA: RDF DATA + MEANING RDF DATA + CONTEXT
  32. 32. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework
  33. 33. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework
  34. 34. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 4. The Semantic Framework
  35. 35. Gonçal Costa – ARC, La Salle October 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
  36. 36. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands 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
  37. 37. Gonçal Costa – ARC, La Salle October 27th -29th, 2015 Eindhoven, The Netherlands Gonçal Costa ARC Engineering & Architecture La Salle, Ramon Llull university Quatre Camins, 2 08022, Barcelona, SPAIN Tel. +34 93 290 24 49 Fax +34 93 290 24 20 E-mail: gcosta@salleurl.edu

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