the use of artificial intelligence in energyefficiency and
TRANSCRIPT
S M A R T E N E R G Y
TheUseofArtificial IntelligenceinEnergy Efficiency andTransactionsforIndustry 4.0andforMicrogrid
ManagementSystems
D r . V i n c e n t S C I A N D R A , C E O
About Us
METRON has imposed itself in the last five years as one of
the most innovative a leading tech companies in energy
for the industry. We were one of the first company
promoting that industrial data was paramount for energy
efficiency, thus industry 4.0 would revolutionize the way
we pilot energy in the factory.
Our vision became a reality, and we are proud today to
optimise industrial facilities world wild. Our teams are
composed of industrial experts, energy engineers, Artificial
intelligence researchers and project managers. They are
committed to the energy efficiency of our clients, and we
are proud to give them quantitative results of our work.
Our Company History
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AninternationalteamOurapproach
Upto2018 2019
OperationalCenters
§ Paris- HQ§ Milan§ Bogota 2020
70+membersofstaff11nationalities
100+clients
§ SãoPaulo§ Dubaï§ Singapore
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We developed a technological platform able to create the
digital twin of any factory, by using existing systems installed
and no additional sensors. Our Artificial Intelligence
modelizes the factory, making it fully transparent in energy.
Once the factory digitalized, we are able to measure the
flexibility of the processes and utilities, making possible to
transact energy on the market or by peer to peer.
By knowing how to optimize the process and utilities and by
being able to forecast the energy consumption, Metron helps
its clients to reduce its carbon footprint by integrating
microgrids technologies as an integrated solution.
Digitalized
Decentralized
Decarbonized
Mission
Our technology enables any factory,
becoming fully transparent in energy.
That are factories able to optimize the full supply chain of
energy thanks to Artificial Intelligence.
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Being able to monitor, modelize and forecast any energy
vectors in the industrial sites, Brings new methodologies
to optimize the factory. Real time optimizations adapted
to the real context of production are now possible with
digitalisation.
1. Energy Efficiency
Vision
Energy Markets are evolving to be able to manages
smaller assets of productions and virtual power plants.
New scenarios of peer to peer trading is developing
around the world.
2. Energy Markets
They have changed our way of consuming energy, but
their resiliency is still a complex equation. However AI
can help manage those resources.
3. Distributed Energy Resources
At METRON we believe that the energy of tomorrow
will drastically be managed differently from today.
This revolution is already on its way. We consider that
those changes will be possible by digitalizing each
electrons and molecules traded. We think the success
of that revolution resides at the crossroads of Energy
efficiency, energy markets and distributed energy
resources.
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Artificial Intelligence What is AI
AUTOMATEDatacollectionand
cleaning
Acquisition,Qualityandreliabilitytests
Processautomation:Digitalizetheassetsandtheircontext
REASON
Reasoning andknowledge beyond human:Understand theenergy andoperation context andcrossit with knowledge bases
ENGAGE
Proactiveactionandcommunication:NaturalLanguage Processing todirectlyinteract with humans andcontrol-commandtooperate themachine
Dataaggregationandcuration
Aggregation,Storage
Dataenrichment
BaselinesandKPIcalculation,Alarmcreation
Standardprocesses
ISO50001,IPMVP
Dataaggregationandcuration
Predictivemaintenance,Productionandstorageoptimisation,Risk
management
Optimisation
Identificationofoptimisationlevies,Prescriptionofimprovement actionswithROI,Ranking ofinfluential parameters,
Benchmarking,Driftdetection,Predictions
Flexibility
Energyassetsmanagement,Simulationofscenarios
Human collaboration
Proactivepersonalized recommendations,Adaptationtouserchoices andcontext,Ongoing
questionsanswering
Machineinteraction
Real-timecontrolandoptimisation,Knowledgecollectedfromthemachine
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Augmented Energy Manager
Follow-up,controlandvalidation
oftheAI’s results andproposals
Continuous improvement
ofAI’s knowledge andperformancesby
continuous feedbackandmanual optimisations
Leading change
byproject managementandcommunicationon
site
Artificial Intelligence
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ThebenefitsArtificial Intelligence
5-15%energy savings12months ROI
40%resources energyoptimisation
SAVINGS
Risks andmistakesreduction
RELIABLE
Volumeincreasingmultisitesdeployment
SCALABLE
Contextualadaptationtouser
preferences
ADAPTED
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METRON-EVA®VPPVirtualPowerPlantDemandResponseandflexibilityoperationTradingwithenergymarketsandbalancingmechanismsAssetsportfoliomanagement
METRON-EVA®MicrogridMicrogrid Management
Energygenerationandconsumptionforecasts
ManagementandcontrolofDistributedEnergyResourcesand
Ancillaryservices
METRON-EVA®OEMSmartMachinePerformanceandup-timeoptimisationPredictivemaintenanceDecentraliseddecision-makingEmbeddedartificialintelligence
Applications Aplatformservingallactorsusingenergy
METRON-EVA®FactoryEnergyTransparentFactoryReal-timemonitoringandforecastofenergyusagesIdentificationofbestsetofparametersandenergyefficiencyprojectsSmartcontrolofindustrialsystems
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Technicaloverview Thedigitalplatform
CloudservicesWebinterface
DataandknowledgeintegrationVisualisation - Exploration- Piloting
EnergyprojectmanagementDataScienceandAItool
OntologiesKnowlege integration
DataScienceMachinelearning
ETL
BigDataUnlimiteddatastorage
ComplexEventProcessing
IIoT
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UseCase Processoptimisation:Meltingfurnace/Glasswaremanufacturer
§ Processing of energy and process datacoming from the furnace supervisionsystem (more than 120 variables)
§ Visualisation of a 12% gasconsumption drift in 2 years
§ Modelling of the variables impact onconsumption:
§ Detection of the most influentialvariables, using a ranking algorithm
§ Determination of the variables forwhich the operator has some flexibilityto pilot (crossing of data and businessknowledge): cullet, electric/gas mix,air/gas mix
§ Definition of the optimal configurationfor these parameters according to D+1production planning so as to maintainthe product quality while minimisingenergy cost
§ Creation of a predictive model toforecast gas consumption based onthe 15 most influential variables (errorrate < 3%)
Actionplan
§ 4.5% reduction in the annualenergy consumption of thefurnace (eq. €250,000/year)
§ Daily optimal settings advice providedto operators: continuous optimisationof the furnace operation andreduction of its energy efficiency drift
§ Prediction of future consumption(accuracy > 97%): operational teamsare notified if an abnormal drift isforecasted
Results&benefits
§ Implementation of an automaticcontrol-command system so that theoptimal regulation of influentialparameters is directly integrated intothe supervision via the METRONplatform
Nextsteps
§ Production of glass flasks for thepharmaceutical industry
§ Perimeter: smelting furnace, themost energy-intensive equipment ofthe plant
>100GWhofgasconsumptionperyear
>>Stake:Limitgasconsumptiondriftsandpilotoperational
parametersofthefurnacewithefficiency
Context
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UseCase Optimisationofelectricself-generation/PaperIndustry
§ Determination of the energyanalysis methodology and validationby the client’s process knowledgeapproach
§ Collection and analysis of more than500,000 points over 1 year
§ Sensitivity study made to theminute granularity (previously madeto the hour): research of theoptimum offset and calculation ofthe associated economic gain
§ Definition of the optimum offset at125 kW (instead of 500 kWpreviously)
§ Implementation of therecommendation and real-timemonitoring of the turbine activity
Actionplan
§ USD 31,000 of annual savingswith no investment
Results&benefits
§ Continuous re-evaluation of theoptimum with context integration(production cycles, energy prices onthe markets…)
§ Dynamic offset control
Nextsteps
§ Plant producing paper from sugarcane residues
§ Perimeter: backpressure steamturbine producing electricity
§ Variation in the electrical siteconsumption happening to quicklyto allow an ideal balance betweenconsumption and production
§ Production of the turbine dependson the plant power consumption(set point = consumption + offset)
§ One-site self-generated electricitythree times cheaper than the gridone but the surplus of producedelectricity is not valued
180GWh ofannualelectricconsumption
>>Stake:Determinetheoptimaloffsetforoperatingtheturbinesoastoreachtheeconomicoptimum
(betterbalancebetweenelectricityimportsandexports)
Context
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Ourapproach microgrid
Optimisemicrogridmanagement
○ Moreaccurateforecastsofenergyproductionandconsumptionoftheassets(basedontheknowledgebases)
○ Real-timebestdecisionmakingbetweenstorage,produceandconsume(off-grid),andsell/buyfromthegrid(on-grid)
○ Dynamicoptimisationandpredictivemaintenance:theevolutionoftheassetsperformanceisintegratedintothealgorithms+predictionofthemomentwhentheprofitabilitythresholdwillnotbeachievedanymore,allowingtotriggeramaintenanceaction.