Presentation by Cenaero

1. NON RESIDENTIAL BUILDING ENERGY
MANAGMENT
MODEL-BASED PREDICTIVE CONTROL FOR BUILDING &
GRIDS
Project Batterie, supported by Wallonia
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

2. Who are we ?
– Private Research Center in Simulation,
Optimisation, Data analytics
• Aerospace & Transport
• Manufacturing & Process industries
• Built environment & Smart Cities
– Stakeholders
• Technological & Numerical industries
• Public & Private decision makers
• Business & Scientific clusters
– Walloon HPC center operator (« zenobe »)
Safran partnership 2007
Creation 2002
HQ in Gosselies
Commercial subsidiary in
Paris
Composite
Workshop
1 Start-up
50+ FTE
50+ % PhD
5+ Mio € of Turnover
Incl. 40% industrial revenues
(2015)
30+ projects
R&D (2015)
20+ Regional SME
collaborations (2015)
Tier-1 HPC center
(14,000+ cœurs)
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

3. Cross-fertilisation … From plane to building
1 Pilot city
(Charleroi)
Energy & Buildings
team
10+ PPP R&D
projects
30+ companies
collaboration (75+ %
PME) 5 (+3) jobs
created
Factories 4.0
Precast performance assessment tools
BIM-to-manufacturing lines (HMI)
Quantity take-off managment (ERP)
Advanced Products & Processes
Energy (Boiler, Ventilation, Fuel cell, …)
Structural/thermal composites & parts
Smart Buildings
Support to Certification
Control & data analytics
Software & App
New built environments
Microclimate in districts
Energy in building stock & grid
Big/geo-data analytics
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

4. Energy in Buildings: challenges & opportunities
• Directive for moving to NZEB @ 2020 ask to
• Reduce heating needs (NWE)
• Increase RES, hence coping with intermittence
• Operation in buildings (diagnostic & maintenance)
• 50+ % of total costs
• May become more effective with IoT low cost/intrusive
emerging solutions
• EU expecting impact for Energy in Buildings
EeB H2020 program : “(…) reducing its (EU) energy consumption by as much as
50%, the construction sector is today on a critical path to help decarbonise the
European economy by 2050”
TEKK Tour Nov. 6th, 2017 Copyright Cenaero
2.6 bi. objects @2017
40% in buildings

5. The 3E strategy
• Combine its expertise in
– NZEB building design
– RES sizing and advice (PV, Wind)
– RES monitoring in operation
• to come out with a new ICT
product
– Building control inBuilding control inBuilding control inBuilding control in operationoperationoperationoperation includingincludingincludingincluding
ideallyideallyideallyideally RES (RES (RES (RES (costcostcostcost////comfortcomfortcomfortcomfort targetstargetstargetstargets))))
– Building blocks cooperation for
flexibility valorisation wrt grid
operator
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6. Interfaces with heating and cooling units
• Non residential buildings with high electrical & RES components
• Solution in a nutshell
• Expected Impacts
• 5 to 30 % yearly energy cost reduction
• Payback time (sensors, flux/flow meterings,…) < 5 years
• Prerequis : open BEM infrastructure
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7. Model Predictive Control (MPC) definition
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

8. – 1. Real-time data (min 1/4h)
• Monitoring effective building state values (= internal
temperature)
• Thermostat setpoint(s)
• Forecasts (e.g. wheather, occupancy plans)
• An aggregation data platform
– 2. Building physical model (e.g. Modelica library)
• Must be dynamic (we play on inertia)
• Must be sufficiently predictive
Model Predictive Control (MPC) solution: the 4
necessary constitutants…
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

9. Advanced phys. models (ex. TRNSYS, E+)
(+) Suited for reliability & long term
prediction
(-) Detailed building properties needed
(-) Hence more variables to calibrate (harder
& stiffer)
No phys. Model: statistical-based trained
models (ex. regression, neuronal,…)
(+) Easy and limited in “métier”
expertise (generic)
(-) Low resilience in case of changes
(-) Needs of larger data set (and
acquisition period)
Basic RC based phys. Models (ex. FastBuildings Lib.)
(+) Suited for basic prediction while till supporting
changes (occupancy, retrofit,…)
(+) Reasonable data quantity needed for
calibration
(+) More flexible for calibration
MPC (cnt’d) the Physical model box approach
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

10. – 3. Optimisation algorithms in two-steps (Minamo)
• Physical model calibration with data (Gap minimisation)
• E-Cost minimization on a 24h-timeline
■ 1 weighted objective (Power x ∆(T_int-T_setpoint),
24h-timeline integrated)
■ 1 actuator parameter = HVAC units power every 1/4h
■ 1 constrain set: Min or Max comfort temperature to do not
transgress around the set point (e.g. ∆T_step +/-2°C)
■ Advanced: Stochastic MPC, distributed optimization for building
flexibility use in DSM (not presented)
– 4. Programmable controllers (e.g. PLC type)
• Through double gateway with the BEM
• Action= Stop/Start HVAC @ 1/4h
Model Predictive Control (MPC) solution: the 4
necessary constitutants (cnt’d)
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

11. Feedback on pilots
Ecoffice passive office building
• 25% energy cost savings to date
• No comfort complaints
• No BMS fine-tuning cost
• No CAPEX requirements
• High uptime
• Less maintenance costs
EPB compliant office building (10+ years)
• 30% energy cost savings
• Increased coworker comfort
• No more human control override issues
Elderly home Le Progrès
• 24/7 occupation
• 15% energy cost savings
• Time savings wrt
operation follow-up
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

12. • Uncertainties on
– The physical model
– The weather & occupant forecast
• Consequences
– Unperfect control = violated constraint
• A pragmatic probabilistic method for real-time
– Error estimation based on the uncertain value using
• Past collected data
• 1 estimating function for time-increase errors
ExternalTemperature
Prediction
Measure
predictions
Past
predictions
measures
Past
measures
Error
database
Scenario estimates
(possible weather
in the predictive
24h window)
ExternalTemperature
Advanced feature: Stochastic MPC
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

13. • A pragmatic probabilistic method for real-time (cnt’d)
– 1 classical (i.e. deterministic) optimization remains but with a massive
increase of parameters
– All scenarios considered = as much as T° variables at each time step
→ lead to 1 single control sequence but robust i.e. less violaƟng the constrain
(min/max de comfort T°)
• Result on 3-month warming on test building
– Same reached target in Energy
– Lower the discomfort hours but in a limited way
STO : Stochastic
DET : Deterministic
PB : Perfect baseline (known
case wo errors)
Advanced feature: Stochastic MPC
TEKK Tour Nov. 6th, 2017 Copyright Cenaero