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

Users Data

Tools

Goals

Systems

Scenario

Procedures

Protocols



Outline







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 Cadastre Climate

Typology Socio-Economic

Energy-related data Semantic Energy

Information Framework

Integrated Platform

ELITE Federation engine

Ontology OWL-DL liteA

URSOS Input form

3D Maps




Codification of an informal vocabulary into an Ontology in a computer-

processable format (i.e. OWL) using Click-On, an ontology editor.


Outline








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

DEMONSTRATION DSS 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 Savona Zaanstad

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

Sensors Building

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


Sensors Building

Ztreamy



Action plans


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


Sensors Building

Ztreamy



Action plans


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


Sensors Building

Ztreamy



Action plans


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


Sensors Building

Ztreamy



Action plans


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


Sensors Building

Ztreamy



Action plans


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


Sensors Building

Ztreamy



Action plans


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


Sensors Building

Ztreamy



Action plans


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


Sensors Building

Ztreamy



Action plans


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 modules Sources

OPTIMUS DSS



4. Defining the







Weather

forecast

Social

media

Energy

prices

De-centralized

sensor (BEMS)

RES

production


Sensors Building

Ztreamy



Action plans


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 modules Sources

OPTIMUS DSS



4. Defining the








Weather

forecast

Social

media

Energy

prices

De-centralized

sensor (BEMS)

RES

production


Sensors Building

Ztreamy



Action plans


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


Sensors Building

Ztreamy


Action plans


Intelligent rules

Mock-up

User validation

Web

interfaces




Developing the DSS end-user 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



4. Defining the








Weather

forecast

Social

media

Energy

prices

De-centralized

sensor (BEMS)

RES

production


Sensors Building

Ztreamy


Action plans


Intelligent rules

Mock-up

User validation

Web

interfaces









out



4. Defining the








Weather

forecast

Social

media

Energy

prices

De-centralized

sensor (BEMS)

RES

production


Sensors Building

Ztreamy


Action plans


Intelligent rules

Mock-up

User validation

Web

interfaces









out



4. Defining the








Weather

forecast

Social

media

Energy

prices

De-centralized

sensor (BEMS)

RES

production


Sensors Building

Ztreamy


Action plans


Intelligent rules

Mock-up

User validation

Web

interfaces









out


Outline








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: [email protected]

Presentations & Public Speaking

Sustainable Places 2015 - The OPTIMUS project