OPTIMUS_ Zöllner

This project is co-funded
by the European Union
Energy Management for Sustainable Action Plans, Brussels, 18 June 2015
Siegfried Zoellner, Coordinator, ICLEI European Secretariat
Optimising Energy Use in Cities through
Smart Decision Support Systems

And with 6 out of 10 people expected to live in cities by
2030, urban energy demands are continuously growing
(WHO).
The challenge: cities are increasingly hungry for energy
• Cities consume over 2/3rds of the world’s energy;
• they are the largest emitters of greenhouse gases;
• and can influence over 70 % of the total ecological footprint.

Local authorities are uniquely placed to play a key role in the
European and national goals of developing a low-carbon economy:
• have a huge sphere of influence and a duty to promote the social,
economic and environmental well-being of their community.
• as permanent bodies that plan for the long term
• a number of data sets available
• portfolios include many types of building,
each which use significant amounts of energy
Using city data to make smart energy decisions

Energy is one of the largest
controllable overheads in many
local authority buildings

The OPTIMUS approach
The OPTIMUS project aims to design, develop and deliver an integrated ICT platform
that will collect & structure open data sets to recommend the best energy-saving
opportunities available in public buildings.
Savona, Sant Cugat,
Zaanstad
DSS
…. using city data to make smart energy-saving decisions in public buildings
The city assessment
framework
Developing the
Decision Support
System (DSS)
Piloting the DSS
three cities
Training sessions
for other cities to
implement the DSS
1. 2. 3. 4.

The SCEAF Model
Smart City Energy Assessment Framework
General characteristics
Political Field of Action
1.1 Degree of ambition
1.2 Efficiency at
fulfilling targets
1.3 Asset Management
Energy & Environmental
Profile
2.1 Energy
Consumption Intensity
2.2 Energy production
via Renewable
Technology
2.3 Energy
Conservation
Issues
2.5 Ambient Air
Pollution
2.4 Network Efficiency
Related Infrastructure
Energy & ICT
3.1 Monitoring Systems
BEMS
3.2 Levels of
integration of
automations, Mart
meters & ICT solutions
3.3 Forecasting
systems
3.4 Exploitation of
Social Media
http://sceaf.optimus-smartcity.eu

Political Field of
Action
Energy &
Environmental Profile
Related
Infrastructures & ICT
OPTIMUS City
City under Evaluation
Highest score in
every indicator
Specific score in
each indicator
Scaling
Calculated score per axis
OPTIMUS Rating Chart

OPTIMUS Decision Support System (DSS)
Weather
forecasting
De-centralized
data
Social
Feedback
Energy
pricing
Renewable
energy sources
Developing the DSS (1/8)
…an overview of the architecture
OPTIMUS DSS will be fed with data sources from multiple domains and it
will support the short-term energy action plans of municipal buildings.

Module 1: Weather Forecasting
Weather can heavily affect energy consumption in a city
(e.g. heating/cooling in public buildings, energy use in
public venues and stadiums, etc.).
By collecting data from weather forecasting we can develop accurate energy-demand
models and map consumer behaviour related to the selected public buildings, which in
turn can be used to help plan and optimise the energy use in the future.
Some of the most relevant parameters that will be taken into account include:
Temperature
Humidity
Wind speed & direction
Solar irradiation
Period of irradiation
Sky coverage
DSS

Module 2: De-centralized Data
Energy monitoring requires a clear definition of the
boundaries of the considered systems and a proper
classification of the captured energy data.
Static data
• Building dimensions;
• Building materials;
• Building destination;
• Occupancy;
• Appliances consuming electricity;
• Instrumentations (e.g. light sensors);
Dynamic data
• Indoor temperature from thermostats;
• Indoor humidity from psychrometers;
• Electricity consumption from counters;
• Natural Gas consumption from counters;
• Thermal flows from calorimeters or other flow meters;
• Occupancy from presence detectors.
Data considered within this module can be distinguished in two main categories:
DSS

Module 3: Social Feedback
Information provided by building occupants can assist the
energy managers in adjusting thermal comfort parameters,
in order to optimize energy use and maintaining comfort
levels in accepted ranges.
The idea is to use input from occupants of the building, reflecting their opinion about
comfort levels in the buildings to help decide what action needs to be taken.
 Thermal Comfort Validator, a tool that evaluates thermal comfort at a 7-level scale.
DSS
http://validator.optimus-smartcity.eu

Module 4: Energy Prices
This data- capturing module extracts real-time data on
energy prices available from energy providers within
the local markets.
Prices will be evaluated for the following energy types:
• Thermal energy (Solar Thermal Energy, District Heating, Natural Gas, Biomass, LPG, others);
• Electricity (PV Energy Production, Wind Energy Production, National Grid Electricity, others).
DSS

Module 5: Energy Production
This module will collect data on the production of energy
from renewable energy facilities available in the city and
will match them with the energy demand profiles in order
to propose the optimal energy management solution.
The energy production is classified according to the kind of source (solar, wind, water
power, renewed biomass, etc.) and the kind of energy produced (electricity, heat).
DSS

User requirements
methods:
• User/group interviews
• Prototyping
• Use cases
• Brainstorming
Sant Cugat
Buildings: 2
Total surface: 18.593m2
Renewable generation: PV plant (23 MWh)
Thermal power: 850 kW
Electrical power: 440 kW
Total energy consumption: 2157kWh/m2
CO2 emissions: 86kgCO2/m2
Logged as: David Hernández Log out
Building description
Building use: Office
Address: Plaça de la Vila, 1
Year of construction: 2007
Surface: 8.593m2
Floors: 6
Occupation: 350 employees, 400 visitors
Configuration
Town hall Status: No action required Last action: 15 / 04 / 2014 8:00
Building description
Building use: Cultural
Surface: 9.593m2
Floors: 6
OPTIMUS DSS – Sant Cugat
Sant Cugat > List of buildings
Partitions
23
Sensors
23
Action plans
3
Configuration
Partitions
8
Sensors
11
Action plans
2
Theatre Status: Action required Last action: 15 / 03 / 2014 8:00
The user interface

The user interface
Logged as: David Hernández Log out
OPTIMUS DSS – Sant Cugat
Sant Cugat > Town Hall > Action Plan: Optimum start/stop of the heating/cooling system
Town Hall
Building use: Office
Surface: 8.593m2
Floors: 6
Energy rating: D
Renewable generation: PV plant (23 MWh)
Thermal power: 850 kW
Electrical power: 440 kW
Total energy consumption: 357kWh/m2
CO2 emissions: 86kgCO2/m2
Action Plan: Optimum start/stop of the heating/cooling system
Description: Control of pre-heating/cooling of the rooms based on schedules and
weather forecasts
Last forecast calculated:
9 / 11 / 2014 8:00

Piloting the DSS (1/2)
Meet the three pilot cities
Savona (Italy)
Zaanstad
(Netherlands)
Sant Cugat del Vallès
(Spain)
This web-based decision support system
(DSS) will be tested and validated through
pilot applications in three different cities:
• Savona (Italy),
• Sant Cugat del Vallès (Spain)
• and Zaanstad (The Netherlands).
OPTIMUS will have, by design, the
necessary degree of generalisation so as
to be easily adapted to cities with different
characteristics.

ZaanstadSant CugatSavona Campus
Proposed actions for energy optimisation
Baseline review using SCEAF
Piloting the DSS (2/2)
The process

 Meetings with:
 The Energy Manager of Savona Municipality
 ARE Project Manager –in charge of Savona SEAP
 School Director
 School Thermal Plant Manager
 Information Collection:
 Municipality’s energy profile & targets set
 Technical characteristics of installed equipment
 School’s operating schedule
Pilot site visits – Savona (1/2)
Participation from:
NTUA, D’APPOLONIA, POLITO, IPS
Checklist of documents:
Colombo-Pertini school energy profile summary
A version of Savona’s SEAP
Energy bills and records, thermal and electricity, past 3 years
Energy Certificate and building plans of school
Operating schedule of school and gym
Technical datasheets of boilers, heating pump, A/C, PV plant
Task 4.1
Application of Smart City ex-ante Assessment
Framework on the cities

Pilot site visits – Savona (2/2)
Detected possibilities to improve energy efficiency
 Space heating
 Domestic Hot Water (DHW) Production
 Lighting system
 PV system maintenance
Action Plan Idea Involved equipment Output of the system
Programming of the space heating
system according to the real energy
needs, to propose an operational
schedule
Sectioning the 4 main heat distribution lines,
installation of thermostatic valves and
variable flow rate distribution pumps.
Schedule for switching on and
off the boilers, definition of
duration and distribution lines to
be switched on and off.
Optimal management of DHW
Production according to the real use of
the school
Use of the main gas boiler, instead of
electricity, installation of timer-driven switches
on remaining electric boilers to operate only
during low tariff times.
A weekly schedule of switching
on/off the boilers
Optimally maintaining the PV of the
school, by comparing the observed
energy production with the expected
Exploitation of the Savona Campus DEMS
that predicts PV energy production
Weekly indication of expected
production
Indicative possible DSS action plans
Task 4.1

 Meetings with:
 Key staff members of the Zaanstad municipality
 BMS company representatives
 Town Hall Thermal Plant Manager
 Information Collection:
 Building’s energy profile
 Technical characteristics of installed equipment
 Town Hall’s operating schedule
 Occupancy levels
Pilot site visits – Zaanstad (1/2)
Electricity and gas records for 2013 and previous years
Energy Certificate and building schematics
Operating schedule of the Town Hall
Technical data sheets of heating pump, ventilation
system, cassette units
Participation from:
NTUA, D’APPOLONIA, POLITO
Task 4.1

Pilot site visits – Zaanstad (2/2)
Programming of the space heating
system according to humidity and
temperature levels
Adjustment of the BEMS accepted levels
of relative humidity and temperature
Passage to a HVAC regulation system
allowing variable flow rate in ventilation
Weekly indication of accepted levels
of relative humidity and
temperature
Change of staff’s working places
according to the cool and the hot areas
of the building
Tracking of employees working positions
via the Flexwhere system
Weekly suggestions of working
places
Change of the base temperature, which
is set at 22o Celsius throughout the
whole year
Adjustment of the BEMS temperature set-
point
Suggestion for setting higher or
lower base temperature
 Space heating
 Domestic Hot Water (DHW) Production
 Occupancy
Task 4.1

 Meetings with:
 The Environmental Manager of Sant Cugat’s
Municipality
 Building Maintenance Technicians
 Energy, Land and Urban Quality Managers
 Technical personnel of the Town Hall and Theatre
 Information Collection
 Municipality’s energy profile and targets set
 Technical characteristics of equipment
Pilot site visits – Sant Cugat (1/2)
Electricity and gas records and bills of previous years
Operating schedule and building schematics
Technical data sheets of equipment
Samples files of the database from the Flexwhere system
Energy production records of the Town Hall’s PV system
Funds spent on purchasing EnEf and RES equipment
Participation from:
NTUA, FUNITEC, D’APPOLONIA, POLITO
Task 4.1

Pilot site visits – Sant Cugat (2/2)
 Space heating and cooling
 Lighting system
 PV system (Town Hall)
 Occupancy
TownHall
Optimization of air-conditioning
profiles using free cooling instead of
the heat pump
Update of the BMS (Desigo system)
Adaptation of air-conditioning electric
load to the present and foreseen
production of the PV plant
Weekly schedule of switching on/off
the air-conditioning system
Scheduling of pre-heating and cooling
of the rooms, according to occupancy
Adaptation of the BMS
Weekly schedule of switching on/off
the respective systems
Theatre
Optimal use of electrical equipment
according to measured and forecast
occupancy (which is highly variable)
Operating Schedules
Installation of BEMS
A weekly schedule based on
upcoming scheduled rehearsals,
past occupancy data, approximate
durations, employees’ habits
Change of staff’s working places
according to the colder and hotter
areas of the building
Operating Schedules
A weekly schedule of working
places based on temperature,
humidity and lighting conditions
Task 4.1

Stay in touch
For the latest news and updates you can join us online
Website: www.optimus-smartcity.eu
Twitter: @OPTIMUS_EU
Newsletter: sign up by visiting our website
YouTube: Optimus-Smarcity.eu
Facebook: OPTIMUS
LinkedIN: OPTIMUS

OPTIMUS Web-based Tools
Thermal Comfort Validator
OPTIMUS SCEAF Tool
http://sceaf.optimus-smartcity.eu/
http://validator.optimus-smartcity.eu/
Assess the performance of a building in
terms of energy optimization, CO2
emissions reduction and energy cost
minimization.
Declare your thermal sensation
inside a building

Contact:
Siegfried Zoellner
Coordinator, Sustainable Resources, Climate & Resilience
ICLEI European Secretariat GmbH (ICLEI Europe)
Leopoldring 3
D-79098 Freiburg
Germany
Tel.: +49-761 - 3 68 92-0
Email: siegfried.zoellner@iclei.org
This project has received funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under Grant Agreement No. 608703.
Thank you for your attention!
www.optimus-smartcity.eu

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