Intelligence Artificielle et performances énergétiques | Axis Parc (LLN) - 27 septembre 2018

Cluster Technology of
Wallonia Energy, Environment
and sustainable Development
1
Axis Parc – 27/09/2018Workshop #3 : Intelligence Ar5ﬁcielle

ICT & Energy ?
3
• Fortes synergies :
o Concepts : #smart - grids / cities / building / mobility…
o Technologies sous-jacentes : (C)EMS, smart
metering, …
• Collaboration INFOPOLE Cluster TIC / TWEED :
o Where ICT meets Energy (2015)
o Cartographie TWEED – smartgrids (2016-2017)
o Digital Energy Business Club (2018)

Digital Energy Business Club
4
• Objectifs :
1. Développement commercial des entreprises membres des
Clusters ainsi que de certains acteurs des secteurs TIC et
énergie.
2. Création de synergies, d’innovations et de partenariats ; entre
entreprises privées, mais également entre entreprises et
investisseurs ou acteurs de R&D.
3. Promotion et développement sectoriel du secteur TIC-énergie.
• Outils :
o Portail ReWallonia.be : cartographies smartgrids / stockage
o Réseaux des Clusters : membres & partenaires
o Projets divers : MeryGrid, E-CLOUD, Interests, …

DEBC - Planning
5
• 16/11/2017 : Blockchains
• 21/02/2018 : Internet of Things
• 07-11/05/2018 : US Matchmaking Mission & Smart
Cities Expo NY
• 27/09/2018 : Machine Learning - AI
• 16-18/10/2018 : IoT, Blockchain & AI Solutions World
Congress | Barcelone

Programme d’aujourd’hui
6
• N–Side : IA et optimisation des performances énergétiques,
tendances et perspectives
• DC Brain : Use case "Réseaux" - Optimisation des flux par l'IA
(gaz, électricité et Chaleur)
• Yazzoom : Use case "O&M" – l'IA pour booster la maintenance
prédictive dans l'industrie
• Optimal Computing : Use case "Efficacité énergétique" –
Prédiction des besoins énergétiques d'une maison & Optimisation
de pompes via IA
• Ingestic : Use case "Efficacité énergétique" - Analyse de la
consommation énergétique des écoles catholiques francophones
• Energis : Use Case "Energis.Cloud", L'outil qui facilite l'accès à
l'IA aux professionnels de l'énergie
• Pitches : Opinum & Thelis Corporate

Besoin d’aide ?
Contactez-nous !
8

Cluster Technology of
Wallonia Energy, Environment
and sustainable Development
TWEED Asbl
Rue Natalis 2 – 4020 Liège – Belgium
Bricout Paul
Project engineer
pbricout@clustertweed.be
Olivier Ulrici
Project engineer
oulrici@clustertweed.be
Cédric Brüll
Director
cbrull@clustertweed.be
9

INFOPOLE Cluster TIC asbl
Rue Godefroid, 5-7| B-5000 Namur - Belgium
Tél. 32(0)81 72 51 41 | Fax 32(0)81 72 51 43
infopole@infopole.be
www.infopole.be
A la recherche d’un partenaire TIC ?
Arnaud Ligot
Président
Sandrine Quoibion
Directrice
Charlie Feron
CommunicaRon & Project Manager

IA et optimisation des performances
énergétiques:
tendances et perspectives
Cluster Tweed
Mont-Saint-Guibert, September 27th, 2018
1
Olivier Devolder
Head of Energy and Industry

How Big Data has delivered for FedEx for 25 years
2
Artificial Intelligence: plenty of success stories
among different industries …
How BMW uses Artificial Intelligence and Big Data to design and
build cars of tomorrow
The amazing ways Google uses deep learning AI
Big pharma turns to AI to speed drug discovery, GSK signs deal
Comment le Deep Learning fait décoller la reconnaissance d’images

3
Asset Replacement and Maintenance
Optimization with Machine Learning
Electricity balancing with
Artificial Intelligence
Dynamic Dimensioning of
Reserve with Machine Learning
Optimized Flexibility Valorization based
on Deep Learning Price Forecasts
Wind farms design and operations
with digital twins and optimization
Situational awareness with
geospatial data analytics
… with strong potential in the energy sector

5
Artificial intelligence is a broader concept than machine learning,
which addresses the use of computers to mimic the cognitive
functions of humans. When machines carry out tasks based on
algorithms in an “intelligent” manner, that is AI.”

7
Machine Learning is a current application of AI based around the
idea that we should really just be able to give machines access
to data and let them learn for themselves, without being
explicitly programmed

9
Artificial Neural network is one group of algorithms used for
machine learning that models the data using graphs of Artificial
Neurons, those neurons are a mathematical model that “mimics
approximately how a neuron in the brain works”.
Neural Network

10
Deep Learning
Deep learning allows computational models that are composed of
multiple neural network layers to learn representations of data with
multiple levels of abstraction

DESCRIPTIVE ANALYTICS
PREDICTIVE ANALYTICS
PRESCRIPTIVE
ANALYTICS
Make Big Data accessible and
manageable
Make predictions supported by Top Artificial
Intelligence/Machine Learning Techniques
Take Optimal data-driven decisions supported by
cutting-edge optimization Algo.
DATA ECOSYSTEM
Improve Data Understanding with
advanced dashboards, KPIs and
Analysis
Using Artificial Intelligence for different objectives
11

AI for a successful Energy Transition 12

Flexible Electricity
Consumptions
13

14
Electricity Production in Germany March 12-March 18 2018
Electricity sector is facing a revolution
where flexibility becomes a key asset…
• Almost all German Demand has to
be covered by Conventional Power
Plant
è Higher Electricity Price
• German demand is more than
covered by Renewable
è Period with negative electricity
price and strong export

Flexible Production
Processes Models
Flexible CHP Models
Flexible Auxiliary
Processes Models
Storage Model
15
Combining different layers of
Advanced Analytics to enable flexibility in industrial sites
DA Price Forecast
Imbalance Price
Forecast
Energy Production
Forecasts
DA/Imbalance
Spread Forecast
Accurate forecasts
+
Investment
Optimization
Planning
Optimization
Nomination
Optimization
Real-Time
Optimization
Efficient mathematical
modelling
Advanced optimization
algorithms
=+
Customized
Energy
Flexibility
Optimization
Platform

Combining different layers of
Advanced Analytics to enable flexibility in industrial sites
DA Price Forecast
Imbalance Price
Forecast
Energy Production
Forecasts
Flexible Production
Processes Models
Flexible CHP Models
Flexible Auxiliary
Processes Models
Storage Model
Investment
Optimization
Planning
Optimization
Nomination
Optimization
Real-Time
Optimization
DA/Imbalance
Spread Forecast
Accurate forecasts Efficient mathematical
modelling
Advanced optimization
algorithms
=+ +
ENERTOP:
Customized
Energy
Flexibility
Optimization
Platform

17
Belgian Spot Electricity Price
Current Week

3 – PREDICTING
Ø Value
Ø Probability
Distribution
1 - TRAINING
Ø Training the model
based on hundreds of
features
2 - VALIDATION
Ø Validation of the
model on historical
data
Present and Past Information
• Imbalance prices
• MIP,MDP
• Reserve volumes and prices
• …
Real-Time Information
• SI, NRV
• Regulation volumes
• Strategic reserves
• …
Forecasts
• Onshore and offshore wind
• Solar per region
Least Squares
Time series
Nearest-
Neighbors
Neural
Networks
Model
Forecast
Forecast Spot Electricity Prices with
Regression Algorithms
18

3 – PREDICTING
Ø Class
Ø Probability
Distribution
1 - TRAINING
Ø Training the model
based on hundreds of
features
2 - VALIDATION
Ø Validation of the
model on historical
data
Anticipate high imbalance exposure with
Classification Algorithms
Present and Past Information
• Imbalance prices
• MIP,MDP
• Reserve volumes and prices
• …
Real-Time Information
• SI, NRV
• Regulation volumes
• Strategic reserves
• …
Forecasts
• Onshore and offshore wind
• Solar per region
Decision trees
Support Vector
Machines
Nearest-
Neighbors
Neural
Networks
Model
Output
class
IMB >> 0
Class: +1
IMB << 0
Class : -1
IM
B
≈ DA
Class : 0
20

21
Scheduler that run on a server
Clean Data Forecast DataModel
Data Handler
Predicting
I
N
T
E
R
F
A
C
E
Storage
REST API
Feature selection
Learning
Django
PostgreSQL
x 500 inputs
Using Machine Learning is not only
about algorithms
Training - Validating
FORECAST

Goals
ENERTOP
Consume
Produce
Model &
Forecast
Modeling of the industrial process and energy markets.
Integration of forecasts for electricity price, demand, etc.
Optimize Integrated optimisation of the consumption and production
Load
Shifting
Load
Scheduling
Load
Shedding
Fuel
Switching
CHP
Modulation
By-product
Optimization
Using Constraint
Programming
Generate optimal flexibility decisions :
Load scheduling with constraint programming
23

1. Model
Generate optimal flexibility decisions :
Load scheduling with constraint programming
24
Accurate results
Fast running
Robust Solution
Intuitive planning
Optimized planningConstraint store
Domain store
Variables +
Domains
Constraint
Constraint
Constraint
Constraint
2. Search
Constraint propagation
Backtracking tree search

Integrated
EU Day-ahead
Electricity Markets
25

26
Intermittent Renewable Electricity Production
leads to a growing importance of import/export at EU level
Low Renewable Production in
Germany but High demand
è Germany is importing
High Renewable Production in
Germany but Low demand
è Germany is exporting

Goals
Model &
Optimize
Couple national markets, maximizes the welfare and optimizes
the network utilization, while respecting complex constraints
High SLA
EUPHEMIA
Used each day to compute the electricity price of the 23 European
countries participating to PCR
Math
Optimisation
2500 days of European Market Coupling
with Euphemia algorithm
Market Cutting-edge Optimization
algorithms to solve
• Large-scale (multi-countries)
• Non-linear (complex network
representation, complex market rules)
• Non-Convex (complex market products)
…problems in limited amount of time
All
over
Europe
to solve
UE market
coupling problem
average daily
value of
matched trades
Of successful
coupling for
DA markets
23
countries
10
min
200
M€
2500
days
28

Smart bidding on electricity markets with
reinforcement learning
• Model free
• Learning optimal sequential behavior/control from
interacting with the environment
29

Smart Local Energy Systems
Dynamic Sizing of
Balancing Reserves
30

31
The risks in the systems become more
variables and needs to be covered by a « dynamic insurance »
Risk of Wind Forecast Error: risk of
having more/less electricity than
expected (Symmetric ?)
Risk of PV Forecast Error: risk
of having more/less electricity
than expected (Symmetric ?)
Risk of power plant outage:
risk of having less elec. than
expected
Risk of HVDC cable outage:
risk of having more elec. than
expected (if exporting)

• Uncertainty in PV and Wind Production
• Uncertainty in Load
32
Forecast Uncertainty Failure and Outage
• Forced outage of power plants
• Failures in the grid (e.g. storms)
What size of Reserve is required to cover the risk ?
Why Reserve Sizing ?

33
• Uncertainty in Renewable Production itself
depends on D-1 Forecast level
• Uncertainty in Load can depend on D-1
expected system state
Forecast Uncertainty Failure and Outage
• Risk of forced outage of power plants
depends on DAM dispatch
• Risk of HVDC failures depends on DAM
dispatch
Incentive to size in a dynamic way instead of on a yearly
basis
Why Dynamic Reserve Sizing ?

34
How to map the system conditions to system
imbalance?
Feature 1 e.g. WIND FORECAST
Step 1: sort the imbalance measures
depending on features (e.g. PV &
WIND)
Historical measures of imbalance
e.g. SI = -25MW
e.g. SI = 122MW
e.g. SI = 172MW
Feature2e.g.PVFORECAST

35
imbalance?
Feature 1 e.g. WIND
FORECAST
STATIC sizing
would just use all
the data without
considering the
features and derive
an average sizing
need

36
imbalance?
Feature 1 e.g. WIND
FORECAST
SCENARIO 2
high wind –
high PV
SCENARIO 1
low wind –
high PV
SCENARIO 3
low wind –
low PV
SCENARIO 4
high wind –
low PV
To make it DYNAMIC,
you need to create
“several scenarios”
• They represent
several situations
that could occur
• To each scenario
are associated past
imbalance data

imbalance?
Feature 1 e.g. WIND
FORECAST
This approach seems to work qualitatively… The
problem is how to quantitatively compute these
scenarios?
• Each features is split in 2? 3? 4?...
• How to compute each interval?
How to design a methodology to compute these
parameters smartly?
Even if an agreement is found on these parameters, how
to automatically update them with new data (lot of
data)?
More features are needed to properly make the
mapping à with this basic method, the number of
scenarios will grow exponentially, e.g. if each features is
cut in 2:
• 2 features à 4 scenarios
• 10 features (which is reasonable) à 1024 scenarios
These are key concerns that
can addressed with MACHINE
LEARNING
What are the issues &
open questions with such
approach?

38
99.9%
reliability
COMPLEXITY
“DISCRETE” MAPPING
“Qualitative
clustering”
“KMEANS
”
“KNN”
Deep Learning
Feature 1
Feature2
Feature 1
Feature2
Feature 1
Feature2
Automatic
and smart
“clustering”
(i.e.
scenarios)
“CONTINUOUS” MAPPING
Local
grouping (no
predefined
scenarios)
Machine learning offers powerful tools to smartly map
the system conditions to imbalance

39
Machine learning tool from conception to production
- 1 –
Algorithm
DESIGN
OUTPUT
theoretical ML
model:
• # clusters?
• Features?
• Parameters
of the
algo…
Algo. 1
Algo. 2
Algo. 3 3
Algo. N
…
KPI 1 KPI k…v Define a list of possible
models
v TRAIN & VALIDATE on
historical data (get the most
out of the data) The models are
assessed and
compared
towards several
KPIs
Algo. x

40
Feature 1
Feature2
- 1 –
Algo.
DESIGN
OUTPUT
theoretical ML
model:
• # clusters?
• Features?
• Parameters
of the
algo…
- 2 -
TRAINING
OUTPUT
The
practical
trained ML
model
DATA INPUTS
Historical data (>1 year)
of SI and system features
(DA forecast of wind,
PV…)
…
…
…
Cluster 2
Cluster 1
Cluster 3
Cluster 4
Cluster 5
…
Up reserve = xxx
Down reserve = xxx

41
- 1 –
Algo
DESIGN
OUTPUT
theoretical
ML model:
• # clusters?
• Features?
• Parameters
of the
algo…
- 2 -
TRAINING
OUTPUT
practical
trained ML
model
- 3 -
PREDICT
ION
The
reserve
sizing
for the
next
day
DATA INPUTS
Historical data (>1 year)
of SI and system features
(DA forecast of wind,
PV…)
DATA INPUTS
Day-ahead system
conditions for the next
day
Feature 1
Feature2
For each hour of tomorrow,
check in which cluster we are.
E.g. Tomorrow at 10am is cluster 3!
Cluster 1
Cluster 2
Cluster 4
Cluster 5
Cluster 3 Up reserve = xxx
Down reserve = xxx
Orange point =
features
prediction for
next day

42
Gains in reliability,
volumes and robustness thanks
to AI-based Dynamic Sizing
Gain in
RELIABILITY
Savings of
VOLUMES
Gain in
ROBUSTNESS
Robust methodology which
remains beneficial & feasible
towards the middle and long
term system conditions:
• Toward 2020
• As well as towards 2027
Positive business case:
• Volume reduction more 85%/time
• Financial gains expected of more
2M€/y (outweighing the
implementation costs)
A better reliability management
Higher FRR during higher risk periods: proper reliability
secured more constantly along the year
1100
1200
1300
1400
1500
1600
1700
static
0,0
50,0
100,0
UPWARD DOWNWARD
High BM scenario 2020 Reference Case 2020
LowBM scenario 2020 Post-Nuclear 2027
Volumesavings
Study conducted for
Belgium by ELIA with N-
SIDE support for Machine
Learning aspects

From Proof of Concept to Industrialized Tool
43
POTENTIAL
ASSESSMENT &
PROOF OF CONCEPT
INDUSTRIALIZATION
CONTINUOUS
IMPROVEMENT
• Identify the potential
• Quantify this potential
• Implement & test several
algorithms with high level
differences
• Assess feasibility of the
approaches
Effort in MACHINE LEARNING
• NOT only consists of
“translating” the PoC!
• Implies fine-tuning, add
extra layers of intelligence,
hybridation… to extract
the full potential
Effort TO BUILD A PLATFORM
• Automatize data
ecosystem management
• Robustness (tool needs to
run every day!): fall-back
solution...
• Build an intuitive interface
• …
• Adapt and improve the
tool as the market changes
continuously
• Improve the method
according to the
technological
improvements
1 2 3

Thank you !
Olivier, Devolder
Head of Energy & Industry
Tel: +32 472 46 83 44
Email: ode@n-side.com
N-SIDE
Avenue Baudouin 1er, N°25
B- 1348 Louvain-la-Neuve
44

Sponsored by : Used by :
DCbrain makes sense of millions of measures brought to us by sensors distributed
in fluid networks such as electricity, gas, or air conditioning.
Our software turns data flows into a real time model of physical networks, thanks to Big Data and Artificial Intelligence
…
DCbrain, Smarter Grids

Secure the network Optimize your operating
budget
Decrease Consumptions
We believe that Artificial Intelligence is the key to answer these stakes
Balancing flows into networks = solving the 3 major issues
of complex networks managers
ü
Classical IT tools can not answer perfectly these stakes
û Very complex engineering tool, not suitable for distributed networks.
û Network management tool concentrated on alerting, not on predicting & optimizing flows and networks
û Low ergonomy /flexibility of basic tools : CMS, GIS, SCADA, BIM…
û Finally, a still important use of Excel, creating many risks (tracking, maintenance, reliability…) and an important
work load.

On top of this technological core, we propose 2 complementary
user interfaces
Digital Network View:
èan exhaustive view on your entire data
èA projection of any unit or quantity present
in the data-set (easy to custom interface)
è Spot incidents in real time
èVisualize scenarios
Dashboard view:
èAutomatize your reporting tasks
èCustomize your dashboards to your
needs
èVisualize information in an optimum way

Artificial Intelligence and flow networks? Our data-driven
approach is Unique
Sub1A Sub1B
Sub2A
Customer Customer
Source
Sub2B
Sub1A Sub1B
Sub2A
Customer Customer
Source
Sub2B
t
t
t
t
t
t
ON/OFF
t
Learn the
map
Learn the edge’s
behavior
Learn the Nods
behavior
Analyse / Alert
Decide
t
t
t
Anomalie Detection. Prediction. Classification. Visualization
à Target and anticipate actions.
à Reduce costs
à Scoring for decision making (Risk, costs …)
Flow propagation. Non-lineary transfer F°. local/global optimum search
à Test hypothesis: Data-driven What if scénario
à Chose the best scenario: Optimum Search
à Plan your network for efficiency & cost-cutting

Active nodes
Deep Neural network
(non linear regression)
Gaz 1 Steam
Water
Gaz 2
non linear transfert fonction
Our Graph-Based Approach

Benchmark the optimized model of DCbrain with reality on every
steam unit in real time.

MiniMix (Minimun Energy Mix) or how do we optimize many
related neural networks
Learn the objective functions
Learn the constraint functions
Minimize it !
Key Success factors :
• Automatized data cleaning pipeline
• Time series modeling
Return on investment: AREVA
• -9% on energy spent
for producing steam :
1M €/year

Take a step back, inject the local optimized models into the graph
and do optimum search at the general level

Deep Flow Engine
Full Stack Approach : Open source bricks linked together by
DCbrain‘s proprietary code & algorithms
§ The Deep Flow Engine is an expert of flows : we replicate the specificities of
topologies in our technocological core (using in particular « graphs of flows »)
§ We have integrated into this graphe layer analytics functionalities, with in house
algorithms

DCbrain, an easy to use tool, dedicated to operational teams
• Integration of all flows & Data in real time (integration of assets,
maintenance, flows, HR datasets...) in a single data repository
• Intuitive and ergonomic visualization interfaces : a BI interface
and a “Digital Model” interface
Modelization of network evolution and automatic impact analysis
o Short term: evolution of charge in the network, planification
of maintenance operations, …
o Medium term: integration of new assets, “crisis”
scenarios,…
Visualize
Analyse
Decide
• Use of our proprietary “flow anomaly detection” algorithm to
identify non-optimal resource allocation
o Non optimized network structure and tuning
o Anomalies : leaks, sensors failure,...
Facilitated and
comprehensive data mining
An easy to use simulation
tool
An optimized network
structure and output

Networks simulationReal time optimization
2 uses cases of DCbrain : Real time optimization and simulation
Deep Flow Engine
• Demand forecast
• Diagnosis of the networks : identification of flow
propagation anomalies
• Optimum finding for production and distribution
• Predictive impact analysis
• Network evolution modelling (new production
points/new clients integration…)
• Automatic scenarios generation (re-routing, flow
propagation computation)
• Optimum identification
Our references :
Impact on exploitation costs and consumptions Optimization of engineering processes

Context : GRDF regional engineering offices have a legal obligation to study the integration of
any Bio-gas projects in less than 2 weeks
They do not have the appropriate modelling tool, capable of rapidly modelling a possible
integration, estimate resilience and costs.
Action:
• Models the gas volume propagation and validate its capacity to be consumed by the existing
network and clients
• Model the existing network and automatically calculate reframing costs, depending on Bio-
gas projects localization
Modelling of gas network extension program

Demand forecast and distribution management
Context:
• This Network is composed of 10 ancient
networks, meshed together 5 years ago, with
one huge incineration plant
• The network is evolving at a fast growing rate
• The management team of the network is
having issues regarding the balancing of the
network and the optimization of the global
output
Action:
• Adaptation of our demand forecast model
(using external weather sources)
• Creation of a propagation model, using 2
years of data
• Implementation of the tool

An intelligent software layer to manage VINCI Energies Micro-grid
Concessions
Learning automatically any
mirco-grid topology
Maximizing Photo-voltaïque
self consumption through
meaningful data-analysis and
prediction algorithms

Client case :
Optimization of network output (steam network)
Context:
• The “La Hague” Site of Areva uses large quantity of
steam to operate its industrial processes
(treatment of nuclear Waste)
• The production is done through 4 old steam
production units
• Data was incomplete
Return on investment:
• -9% on energy spent for producing steam è 1M€/year saved
Action:
• Analysis of data and recreation of key
indicator(eg global network output)
• Analysis of each production unit output
• Creation of a model correlating demand and
output
• Use of a model to optimize production according
to demand

Mob : +33 7 68276672
Mail : thomas.bibette@dcbrain.com
Address : DC Brain, 23 avenue d’Italie, 75013 Paris
Thomas Bibette
Export Manager
Thank you !

creating value from data
Operations & Maintenance
l'IA pour booster la maintenance prédictive dans l'industrie
Alexis Piron – 27/09/2018

vous aide a creer de la valeur a partir de vos donnees
7Annees d’Expertise
11Personnes
Science des
Donnees
I.A – Machine Learning
Prescrip<ve Analy<cs
Detec<on d’Anomalies
Data et Process Mining
Ingenierie
Mechatronique
Physique
Vision et Capteurs
Régulation Industrielle et contrôle
Solutions
Consultance et Services
Projets Complets
Systemes d’aide a la decision
Logiciels sur mesure

Traditional monitoring of Industrial assets 2. Descrip7ve sta7s7cs
3. Dedicated sensors
1. Hand-wri>en rules
4. KPI’s and dashboards

Some problems…
• You can’t write rules for every issue or sensor:
• there’s no 5me
• you don’t know what could go wrong
• Problems happen in the so=ware execu5on / in the communica5on
with someone else’s equipment.
• Not every issue is a vibra5on.
• Sta5s5cal analysis doesn’t always work (highly
dynamical or complex signals)

What changed?
Machines are getting more
complex
More data is available
- Sensors, software logs, contextual data, lab
measurements…
- Easier to collect (IoT etc)
- No storage / speed limits
Machine Learning and AI are
mature techs

brings AI based anomaly detection to any data monitoring platform
Yanomaly contains mul.ple anomaly detec.on algorithms
• Some are implementa.on of well known techniques, others are proprietary
• They are either univariate or mul&variate (or process mining-based)

Compared to tradi,onal monitoring
Detect unknow
problems, abnormal
trends and pa8erns,
external sources
Detect problems that
are not vibrations
Scale easily to more
data, new sensors,
changes, variable
components or
subsystems…
Detect issues in
software execution
in advanced
machinery
Handle complex signals
with better detection of
issues and less false
alarms
Take context into account
(machine state,
temperature, raw material
proper,es, recipe)

Service
O&M
• Look at anomaly scores just prior to start of issue
• More efficient investigation and troubleshooting
• Reduce the mean time to repair of your service team
• Real-time detection of anomalies
• Incident prevention: avoid down-time
• Speed up root diagnostics for faster recovery
Two main use cases

Univariate anomaly detection algorithm example
• Compute features describing the signal’s “shape”
• Compare current feature values with previous one à compute
anomaly score
Time between pulses longer than usual
Abnormal signal amplitude

Mul$variate anomaly detec$on algorithm example
• Predict the signal of a sensor based on the signals of mul4ple other
sensors
• Compare the predic4on with the actual value à compute anomaly
score

Pourquoi pour
• Modèle OEM de licence
• U3lisable par des non-data scien3sts
• Performance de détec3on vs
fausses alarmes

creating value from data
Alexis.Piron@yazzoom.com
0486/80.10.43
www.yanomaly.be

Copyright © 2007-2018 Optimal Computing S.P.R.L. All Right Reserved
OPTIMAL COMPUTING
www.optimalcomputing.be
stephane.pierret@optimalcomputing.be
Use cases “Efficacité énergétique” –
Prédiction des besoins énergétique
d’une maison & Optimisation de pompes
via IA
Digital Energy Business &
Technology Club

Agenda
Use case 1 : Prédiction des besoins énergétique d’une maison
passive via IA
Use case 2 : Optimisation de pompes via IA
2

Prédiction de la consommation en chauffage d’une
maison passive
3
Maison passive équipée de panneaux
solaires intégrés au bâtiment
Chauffage appoint:
Pompe à chaleur air/air dans le living
Chauffage et climatisation
+ Post chauffe électrique sur ventilation
40 mesures principales (+50)
Températures intérieures (9)
Consommation d’énergie (9)
Qualité de l’air intérieur (3),
Production d’énergie (4 + 30),
Conditions météo (8 + 10)
Températures panneaux (10)
Supported by the European
Commission’s Seventh Framework
Programme

Equipement de mesure
http://www.optimalcomputing.be

Pourquoi prédire la consommation ?
5
1. Quand stocker ou injecter sur le
réseau ou consommer du réseau?
2. Détecter des problèmes dans la
régulation ou l’utilisation
1. Pour augmenter l’auto consommation
2. Pour diminuer la consommation
et donc
Diminuer la consommation annuelle
Summer WinterWinter SummerSummer

Copyright © 2007-2018 Optimal Computing S.P.R.L. All Right Reserved 6
Feed Forward Artificial Neural Network
∑ A
x1
x2
x3
w1
w2
w3 Summation
Activation
Function
Synaptic
weights
May have many layers
Different types of Layers
Different types of activation functions
Learn the weight using back-propagation
Ce que l’on veut prédire?
Consommation de la pompe à chaleur J+1
En utilisant quelles données?
Température de consigne du Living J+1
Température du Living J
Température extérieure J+1
Radiation solaire J+1
Entrainement du réseau de neurones
Base de données sur 700 jours
Utilisation du réseau pour la prédiction
Température de consigne du Living J+1
Température du Living J
Température extérieure J+1 (prévision météo)
Radiation solaire J+1 (prévision météo)

Prédiction du réseau de neurones
7
Summer WinterWinter SummerSummer

Use case 2 : Optimisation de turbomachines via IA
8

Possible Optimization Methodologies
9
Simulation
Design Variables Responses Time consuming, difficult to
deal with lots of design
variables and responses, …A
Make use of Gradient
Requires simulation code
modification, local method,
noise !, Constraints !,
multiple objectives !, …
Simulation
Design Variables Responses
B
Simulation
Design Variables Responses Black box, global
optimization, multiple
objectives, uncomputable,
learningC

Optimization Algorithm
10
Design of
Experiment
Simulation
DB
Neural
Network
Training
Genetic
Algorithm
Neural
Network
Inner Optimization
Outer Optimization
Simulation
This is a learning process with 3 key elements DB Neural
Network
Genetic
Algorithm

CFD Based Shape Optimization
11
Axial Fan
External Diameter
Fluid
Compressibility
RPM
Flow Rate
Flow Rate Range
Peak Efficiency
Total Pressure
Power
Rotor blades
Stator blades
310 mm
Air
Incompressible
4500
2,7 m3/s (at peak efficiency)
[1,25 m3/s; 5,0 m3/s]
87,26%
4350 Pa (at peak efficiency)
13,4 kW (at peak efficiency)
20
15
CFD
Mesh type
Rotor Mesh
Stator Mesh
Flow type
Unstructured mesh
55 734 cells
51 344 cells
Steady Flow
Turbulent

Workflow Outline
12
Geometry / CAD
Free Form Deformation
Simulation Connectors
Python scripts
Optimization Algorithms
Neural Network, Genetic Algorithms
Original Geometry
STL files
Pre-processor
CFD
Post-processor

Optimization Results
13
Design variables
12 Parameters
Free Form Deformation
Maximize efficiency at
3 operating points
Objectives
Key Data
# CPU cores
Hardware
CFD Calculation Time
# CFD simulation
Optimization time
Performance increase
8
Single Desktop PC i7
10 minutes
112
19 h
+0,8 % on averaged
+2% at high volume
flow

Axial Fan Performance Results
14
+2%
-4,5%

House Multi-Objective Optimization
15
Goal : Optimization the construction cost versus the energy consumption
Tools :
PHPP software and project
PHPP enhanced by a construction
cost calculator
Xtreme Multi Objective Optimization

QUESTIONS ?
16
OPTIMAL COMPUTING
www.optimalcomputing.be
stephane.pierret@optimalcomputing.be

DATA & IT SERVICES TO MAKE YOUR BUSINESS PRODUCTIVEDATA & IT SERVICES TO MAKE YOUR BUSINESS MORE PRODUCTIVE

INGESTIC SPRL
Founded on: 05/2011
Size: 30 FTE
Offices: Rue de Rodeuhaie 1, 1348 Louvain-La-Neuve
services to improve your information systems
and make your data productive

COMUNERIS
SMART DATA
SMART DATA LEGAL ADVISORY
Enterprise architecture
definition
Creation of the future
application – together
Selection of the
software/hardware suppliers –
independently
Change
management
Training
Functional
coordination
Technical
functional analysis
Applicative
architecture
Help To Define
Help To Build
Help To Use

COMUNERIS
SMART DATA
SMART DATA LEGAL ADVISORY
Data science
Data mining
Data quality
Machine learning,
Artificial Intelligence

OUR EXPERIENCE
Utilities
Industries 4.0
Industry
Big Data
Data Science
Data Analysis
Smart Data
Smart Cities Energy
Functional Coordination
Business Analysis
Enterprise Architecture
Data Quality
Functional Analysis
Testing
Smart Meter
Smart Grid
Mobility
IA
Machine Learning
Smart Cities
Energy-
Efficiency
Projet pilote
en cours
IIoT
Smart Data

Data Science : What can we do ?

Case : Calcul du potentiel d’économie d’énergie des écoles

Quel est le potentiel d’économie d’énergie d’une école ?
Consommation d’électricité par élève
Consommation de gaz
par élève
Données récoltées pour
• 275 000 élèves
• Environ 1000 établissements
• 49 GWh d’électricité / an
• 444 GWh de gaz / an
• ~ 100 établissements avec
compteur d’électricité quart-
horaire

Les différences entre écoles viennent de gestions différentes,
mais aussi d’activités différentes
Consommation
de nuit élevée
Consomma0on
de nuit basse
Réduction le mercredi
après-midi
Consommation très basse
pendant les congés
Consommation légèrement réduite
pendant les congés

Consommation
tard en soirée
Consomma.on
tôt le ma.n
Mercredi bas,
aussi en matinée
Jours de semaine
semblables

Le modèle d’économie d’énergie identifie les meilleures performances énergétiques,
en tenant compte des différences d’activités
Les quelques données disponibles sur les établissements sont peu informatives
Les differences entre niveaux, options, lieux, tailles, sont faibles au regard de la variabilité des données
Comme indicateurs des différentes activités, le modèle utilise la consommation de moments spécifiques
Features : consommation de jour, de nuit, avant ou après les cours, le week-end, etc.
Comme mesure de gestion performante, le modèle utilise certains niveaux de consommation des écoles
performantes
En combinant ces informations, le modèle produit un profil de consommation idéal par école

CONTACT
0476/81.21.81
paul.courtoy@ingestic.be
Paul COURTOY
Business Developer
Thank you

Tweed
27 / 09 / 2018
Energis.Cloud
makes AI easy
for Energy professionals
ENERGIS
by Romain Hollanders

Energis.Cloud makes AI easy for Energy professionals
Enable rational use of energy © 2017 Energis sa/nv | www.energis.cloud Pag. 2
Scholars Practitioners
Develop super powerful but
complex algorithms
At ease with statistical and
technical tools
Algorithms often designed for
theoretical data sets
Have easy access to actual data
sets
Would greatly benefit from AI
tools to analyse their data
But will not use it unless it is
super easy to use and interpret

What is Energis.Cloud ?
END-USERS

At Energis, we specialize in ICT solutions.
Our aim is to optimise the energy performance of buildings
by empowering Energy Experts with the most innovative technologies.
2015 – 2016
Energis 3.0
+Forecast & ICP
2013 – 2014
Energis 2.0
+M&V
2010 – 2012
Energis 1.0
Monitoring
2017
Energis 4.0
Smart Energy Platform
Pag. 4
Angelo Santoro
CEO and Founder
Frederic Wauters
Co-founder
and Product Manager
Lisiane Goffaux
Co-founder
and CEO of Freemind
Mario Rubino
Co-founder
and Country Manager Italy
Enable rational use of energy © 2017 Energis sa/nv | www.energis.cloud
A bit of history

Our mission is to bring value in the energy
efficiency market by working together with
organizations such as: Utilities, ESCOs,
engineering companies, energy managers, facility
managers, etc.
We do NOT sell directly to end users but we
position ourselves as your technological partner
for you to create a highly competitive energy
management offer.
Pag. 5Enable rational use of energy © 2017 Energis sa/nv | www.energis.cloud
We are your technological partner

16/10/2018
(c) 2018 Energis SA | www.energis.cloud | EBM3
22032018
6
Who are we ?
ENTER ENERGIS.CLOUD

Energis.Cloud
Advanced Analysis Optimisation &
Control
Bi-directional Data Communication
Standard Analysis
Pag. 8Enable rational use of energy © 2018 Energis sa/nv | www.energis.cloud Energis Overview
Invoice Analysis
Invoices/
Contracts
DSO
MMR AMR Data
Loggers
BMS/
SCADA
IoT Raspicy
Weather
Services
File Upload

What is a model in Energis.Cloud ?
Enable rational use of energy Pag. 10
Output data (typically consumption data) Input data (temperature, occupancy, ...)
!(#) %& # , %( # , …
*! # = ,(%& # , %( # , …) ≈ !(#)

What is the purpose of a model ?
Quantify and verify energy savings
(Measurement & Verification)
Track performance of site or
equipment
Make forecasts and budgets
Interrogate to answer questions
(past, present, future)
Alert about missing/outlier data and
correct the data
Optimisation & control of systems
?

Types of models
White-Box Model
(physics-based)
Grey-Box Model
(semi physics-based)
Black-Box Model
(machine learning)
!" # = %('( # , '* # , …) !" # = %('( # , '* # , …)!" # = %('( # , '* # , …)
general structure is known
parameters are unknown
fully known unknown

IPMVP ID Card

Model Identification within a few clicks
1 Select the Y data
2
Select the
modelling
period
3 Select the granularity
4
Select the
X data
5 Identify

?
What Energis.Cloud does next
HDD
Humidity
Irradiation
Occupancy
…
? Which input variables ?
? Which functions ?
? Which regimes ?
Many possibilities
Machine Learning magics
f
HDD,
Humidity,
Irradiation,
Occupancy,
...

We go much further than linear regression...

Many different patterns can be recognized

We also recognize many recurrent patterns
Seasonal effect Weekly effects
And more...

Result is evaluated using the IPMVP criteriaResult is fast and precise

Advanced Analysis
Optimisation
& Control
Bi-directional Data Communication
Standard
Analysis
Invoice
Analysis
It is ready to use

Energy Performance
Contract
Spotlight:
AZ Sint Lucas Hospital
Floor surface: 40.000 m²
Number of beds: 412
Electricity:
Gas:
Cost:
7 GWh / yr
4 GWh / yr
750 k€ / yr
Goal:
• -15% gas
• -10% electricity
Contract:
5 years (2017 – 2021)
Energy efficiency project
with low investment
“No cure no pay”

Follow-up of
photovoltaic installations
80+ monitored sites
30+ identified models
300+ production alerts per year
60% of which have led to an intervention
A few key values

Energis.Cloud makes AI easy for Energy professionals

CONTACT US
Thank you !
VISIT
www.energis.cloud
CONTACT ME
Romain Hollanders
romain.hollanders@energis.cloud
or: info@energis.cloud
WHERE TO FIND US
Brussels (BE)
Ottignies-Louvain-la-Neuve – HQ (BE)
Caserta (IT)

Energis.Cloud decomposes the data into 3 sets
Training set
Validation set
Testing set
Outliers
Training set used to evaluate individual models
Validation set used to select the best model
Testing set used to evaluate the IPMVP criteria

Data Platform for
Energy &
Environmental Actors
IA et optimisation des
performances
énergétiques
Alexis ISAAC

Opinum contributes to a better environment and to the energy transition with a
digital platform letting energy and environmental actors leverage the untapped
potential of data

aaScalable aaFlexible aaSecure
Backbone of your digital transformation

Eoly
Eoly is the power utility company of Colruyt. The company is in charge of the
group zero carbon emission target as well as selling energy their produce through
renewable power.
As the Energy Management Platform (EMP) of Eoly, Opisense :
• Improve the efficiency of the operation team by better predict issues on
renewable assets
• Drastically extend data analytics capabilities of the energy trading team
• Foster innovation in terms of energy management of their assets (building, EV,
local renewable power)
• Accelerate the development of new digital services for their client
Eoly is an happy customer of Opisense since June 2017
More than ~6000 data sources are connected to Opisense combining data from
Wind turbine, invoices, sub metering, solar, biomass, open data…
Creation of ~200.000.000 data points each month
553 stores
687.000 m²
30.000 employees
581 Structures affiliated (BE & FR)

üR coding environment
üPreview result directly in the platform
üConnect to your coding tool
üDeploy your own processing environment
Your own algorithms
Calculated Variables
Datasources </code R>

Total is transforming its business to become a global energy company. In this
context, they massively invest in new business line such as renewable and power
& gas supply through their division GRP (Gas Renewable & Power).
Total decided to opt for Opisense to leverage the untapped potential of data
produced by either their own assets or clients. Call EMP (Energy Management
Platform) by Total, the platform handle metering and invoice data from SAP,
Salesforces, sub-metering system, smart meters, solar, …
The solution is use by Total and by subsidiaries like Lampiris, Greenflex and BHC.
Total is an happy customer of Opisense since June 2016
Hundreds of thousands of data sources are connected to Opisense combining
data from smart meters, invoices, sub metering, solar, open data…

üRestful API
üPowerful authorization scheme handle by the
platform
üOpen source application to kick start your portal
https://github.com/opinum
Build on top of Opisense
Build on API
A
P
I
Sitecare…

MERCI !
Contact : Alexis ISAAC
Head of Business Development
Rue Emile Francqui, 6 – 1435 Mont-Saint Guibert
Phone +32 (0)2 340 19 23
Mobile +32 (0)486 69 69 89
Mail ais@opinum.com
Web www.opinum.com

RESEAU IA
Le Collectif pour la Wallonie
• Accélérer le développement économique de la Wallonie
• Réponses simples et pragma7ques
• Pour les acteurs privés et publics

RESEAU IA
• Rassembler l'exper-se
• Rendre visible (vitrine wallonne de l’IA)
• Rendre lisible (l'écosystème)
• Renforcer la chaine de valeurs
• Experts de spécialités complémentaires
• Lien entre ini-a-ves et organismes existants
• Simpliﬁer l'accès à l'informa-on

RESEAU IA
• Déﬁnir les axes importants
• Forma2ons
• Priorités régionales
• Economiques et Éthique
• Présenter l'oﬀre de forma2on
• Ini2ale et Con2nuée
• Technique et Business
• Partage d’expériences

RESEAU IA
• Présence digital
• Label « Reseau IA » sur Digital Wallonia
• Site internet www.reseauia.be
• Groupe LinkedIn
• Conférences
• IniBée par le groupe (14 Novembre à Liège)
• Relais vers l’agenda de l’IA
• Proposer orateurs (UCM en janvier)
• Ateliers
• Entre experts IA (partage d’expériences)
• TransformaBon business

RESEAU IA
• Membres= experts de l’IA
• Technique
• Business
• Légal
• Financement
• Fiscalité
Adhésion par la signature de la charte et NDA
• Les clients de l’IA
• Entreprises
• Communes
• Politiques
Soumettre sa question, son projet (NDA possible)

RESEAU IA
Site internet: www.reseauia.be
Mail: contact@reseauia.be
Tél: 081/402891
Localisa>on:

Intelligence Artificielle et performances énergétiques | Axis Parc (LLN) - 27 septembre 2018

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