Sustainable Places 2015 - The OPTIMUS project

Building a semantic-based decision support
system to optimize the energy use in public
buildings: the OPTIMUS project
Álvaro Sicilia, Leandro Madrazo, Gonçal Costa
ARC Engineering and Architecture La Salle
Ramon Llull University, Barcelona, Spain

Álvaro Sicilia – ARC, La Salle
Outline
1. “Smart cities” paradigm for decision making
2. SEMANCO: the process to create a semantic-based
platform to support decision making
3. The development of the OPTIMUS DSS
4. 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
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
Systems

STAR-CITY: Semantic Traffic Analytics and Reasoning for CITY [1]
a cognitive system that computes possible explanations of traffic congestions.
STAR-CITY integrates heterogeneous data in terms of format variety (structured and
unstructured data), velocity (static and stream data) and volume (large amount of historical data).
[1] Freddy Lécué, Anika Schumann, Marco Luca Sbodio: Applying Semantic Web Technologies for
Diagnosing Road Traffic Congestions. International Semantic Web Conference (2) 2012: 114-130
from: http://www.vtce.altervista.org/
The traffic data from Dublin City Council Traffic
Department.
Events data (concerts, conferences, etc.) collected
from public Web sites such as Eventbrite.
Specific events such as road works and accidents
from Twitter
CSV files, tweets, XML, ESRI shapes…
OWL 2 EL profile
W3C Time Ontology, W3C Geo Ontology
http://researcher.watson.ibm.com/researcher/view_group.php?id=5101

EECITIES: Service platform to support planning of energy efficient cities [2]
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)
[2] 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.
- …

…data interoperability is not only a technological challenge.
the technological solutions need to be embedded in scenarios which encompass
strategic goals, users and systems, along with the tools to analyse the data
It also involves devising and applying procedures to avoid doing
redundant work, to reduce design errors, and to be replicable in other contexts
UsersData
Tools
Goals
Systems
Scenario
Procedures
Protocols

a semantic-based integrated platform to support
planning of energy efficient urban areas, with the
participation of the different stakeholders involved:
planners, policy makers and energy consultants
2. SEMANCO: the process to create a semantic-based platform
to support decision making

Cluster view
Performance indicators filtering
Table view
Multiple scale visualization
Multi-criteria analysis

URSOS Energy
calculation engine
GIS data
Census CadastreClimate
Typology Socio-Economic
Energy-related data Semantic Energy
Information Framework
Integrated Platform
ELITE
Federation engine
Ontology
OWL-DL liteA
URSOS Input form3D Maps

Codification of an informal vocabulary into an Ontology in a computer-
processable format (i.e. OWL) using Click-On, an ontology editor.

The goal of the OPTIMUS project is to help local authorities to
optimise the energy performance of public buildings by applying
the short-term actions suggested by a Decision Support System
(DSS) which handles data obtained in a diversity of sources and domains:
- Weather conditions
- Social behaviour
- Building energy performance
- Energy prices
- Renewable energy production

The OPTIMUS DSS optimizes:
• 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.

The innovative approach of the OPTIMUS DSS is based on:
• The use of multidisciplinary data sources
• The semantic modelling of data using Semantic Web technologies
• The integration of data for energy optimization

Semantic framework
Weather
forecastin
g
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

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

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

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

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
Sant Cugat
Savona
Zaanstad
The results of the implementation
of the actions in each pilot city will
modify the data sources
DEMONSTRATIONDSS INTERFACE

The development process of the OPTIMUS DSS benefits from the experience and
results obtained in the SEMANCO project:
1. the tools used to create an ontology such as the editor Click-On
2. The SEMANCO ontology itself which has been enhanced with the dynamic
data required by the OPTIMUS project.
SEMANCO
long-term decisions
static data
OPTIMUS
short-term decisions
dynamic data

4. Defining the
application scenarios
2. Capturing the user requirements
1. Defining the context
Sant-Cugat SavonaZaanstad
3. Identifying dynamic data sources OPTIMUS Ontology
Creating the semantic framework
Creating the DSS Engine
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
Semantic Integration
SensorsBuilding
Ztreamy
Publish and subscribe
Developing the DSS end-user Interfaces
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
Political Field of Action
Energy & Environmental Profile
Related Infrastructures, Energy & ICT
OPTIMUS development
Four design stages Three developments

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
To highlight the strengths, the vulnerabilities and the
opportunities of the city in which the DSS is going to be
used.
A Smart City Energy Assessment Framework
(SCEAF) has been developed to estimate the energy
efficient measures based on the suggestions of a
decision support system or of an energy management
strategy.
A list of KPIs has been created to evaluate the DSS.

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
The requirements are captured by means of surveys and
mock-ups of the user interfaces which are discussed
with the end-users to verify with them the specified
requirements and functionalities.

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
identify the static and dynamic data
sources that might be needed to meet
the user requirements.
The data items of the sources are
characterized.

Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
4. Defining the
A scenario describes the components that the DSS needs to suggest the end-user a short-
term action: input data, prediction models, intelligent rules, action plans and KPIs.
For example, a scenario would be: “To optimize the boost time of the heating/cooling
system taking into account the forecasting of the outdoor/indoor air temperature and the
occupancy of the building”
Data
Prediction
models
Users
Action
plans
Intelligent
rules

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
Four design stages Three developments
OPTIMUS development

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
1. Define a global ontology to model the data sources (i.e. Dynamic such as sensors and
static such as building and system features).
• 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
Sensors
(based on SSN ontology)
OPTIMUS ontology
Building & systems features
(based on Semanco ontology)

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
1. Define a global ontology to model the data sources (i.e. Dynamic such as sensors and
static such as building and system features).
• Static data (Building and systems features) can be modelled with an ontology extended from
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
Sensors
(based on SSN ontology)
OPTIMUS ontology
Building & systems features
(based on Semanco ontology)

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
2. Implement integration methods based
on pub/sub systems (e.g. Ztreamy).
Triple store
(Virtuoso
Server)
Publisher
Semantic Framework
Weather data
De-centralized
data
Social media
Ztreamy
Server
Energy prices
Energy
production
Semantic
Service
Publisher
Publisher
Publisher
Publisher
Subscriber
Data capturing modulesSources
OPTIMUS DSS

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
2. Implement integration methods based
on pub/sub systems (e.g. Ztreamy).
Triple store
(Virtuoso
Server)
Publisher
Semantic Framework
Weather data
De-centralized
data
Social media
Ztreamy
Server
Energy prices
Energy
production
Semantic
Service
Publisher
Publisher
Publisher
Publisher
RAW
DATA
RDF DATA:
RDF DATA +
MEANING
RDF DATA +
CONTEXT
INTEGRATED
DATA
Subscriber
Data capturing modulesSources
OPTIMUS DSS

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
At the core of the DSS engine lie the intelligent rules, a set of procedures
to check if the input data match certain patterns to derive an action plan.
The intelligent rules are fed with predicted, real-time, and static data.
Prediction models to forecast:
- energy consumption of a building
- indoor air temperature
- energy prices
- energy production of photovoltaics panels

4. Defining the
Weather
forecast
Social
media
Energy
prices
De-centralized
sensor (BEMS)
RES
production
SensorsBuilding
Ztreamy
Action plans
Prediction models
Indicators
Intelligent rules
Mock-up
User validation
Web
interfaces
1. Mock-ups are developed and discussed
with the end-users in several iterations
2. When a consensus is reached, the mock-up
is implemented in the DSS
3. An evaluation with the end-users is carried
out

A first prototype of the DSS has been created based on the findings of a requirements
analysis conducted in three pilot cities
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
A first prototype of the DSS has been developed including three action plans, four
prediction models, and data integrated from five sources. It will be applied in the three
pilot cities, operating under real working conditions
Requirements capturing is not a one-time process. It needs to be continuously validated
with end-users and domain experts

We (consortium) are discussing how to publish the main assets
of the project in order to be reused/extended by third-parties:
- OPTIMUS ontology
- Semantic Framework (based on Ztreamy system)
- DSS engine (Intelligent rules, prediction models…)
- Data collected (RDF)
- Mobile App

Álvaro Sicilia
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: asicilia@salleurl.edu

Sustainable Places 2015 - The OPTIMUS project

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