Table Of ContentMACHINE LEARNING
AND DATA SCIENCE
IN THE POWER
GENERATION
INDUSTRY
Dedicated to my wife Fahmida Ahmed Bangert
whose patience and understanding enabled this book to be written.
MACHINE LEARNING
AND DATA SCIENCE
IN THE POWER
GENERATION
INDUSTRY
Best Practices, Tools,
and Case Studies
Edited by
PATRICK BANGERT
Artificial Intelligence, Samsung SDSA,
San Jose, CA, United States
Algorithmica Technologies GmbH,
Bad Nauheim, Germany
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Contributors
A.BagnascoDepartmentofElectrical,Electronic,Telecommunication
EngineeringandNavalArchitecture(DITEN),UniversityofGenova;
IESolutionsSoluzioniIntelligentiperl’Energia,Genova,Italy
PatrickBangertArtificialIntelligence,SamsungSDSA,SanJose,CA,
UnitedStates;AlgorithmicaTechnologiesGmbH,BadNauheim,
Germany
DanielBrennerWeidmu€llerMonitoringSystemsGmbH,Dresden,
Germany
CristinaCornaroDepartmentofEnterpriseEngineering;CHOSE,
UniversityofRomeTorVergata,Rome,Italy
JimCromptonColoradoSchoolofMines,Golden,CO,UnitedStates
PeterDabrowskiDigitalizationatWintershallDea,Hamburg,Germany
F.FresiGruppoHumanitas,ClinicaCellini,Torino,Italy
RobertMaglalangValueChainOptimizationatPhillips66,Houston,TX,
UnitedStates
DavidMoserEURACResearch,Bolzano,Italy
StewartNicholsonPrimexProcessSpecialists,Warrington,PA,United
States
MarcoPierroDepartmentofEnterpriseEngineering,UniversityofRome
TorVergata,Rome;EURACResearch,Bolzano,Italy
M.SaviozziDepartmentofElectrical,Electronic,Telecommunication
EngineeringandNavalArchitecture(DITEN),UniversityofGenova,
Genova,Italy
F.SilvestroDepartmentofElectrical,Electronic,Telecommunication
EngineeringandNavalArchitecture(DITEN),UniversityofGenova,
Genova,Italy
DietmarTilchZFFriedrichshafenAG,LohramMain,Germany
A.VinciIESolutionsSoluzioniIntelligentiperl’Energia,Genova,Italy
xi
Foreword
Thepowergenerationindustryisacomplexnetworkofpower
plants running on various fuels, grids distributing the electricity,
and a host of consumers. Many challenges exist from predicting
physical events with equipment or the availability of fuels such
assolarradiationorwind.Theyalsorangeoverissuesofbalancing
the smart grid and negotiating electricity prices.
Manyofthesechallengescanbesolvediftheunderlyingmech-
anismisexposedinnumericaldataandencapsulatedinthelan-
guageofmathematicalformulas.Itisthepurposeofdatascience
toobtain,clean,andcuratethedatanecessaryandthepurposeof
machine learning to produce the formulas.Once these exist and
areverifiedtobecorrect,theyareoftenthemselvesthesolutionor
can be readily convertedinto ananswer to the challenges.
This book will present an overview of data science and
machine learning and point toward the many use cases in the
power industry where these have already solved problems. The
hype surrounding machine learning will hopefully be cleared
up as this book focusses on what can realistically be done with
existing tools.
The book is intended for any person working in the power
industry who wants to understand what machine learning is
andhowitappliestotheindustry.Itisalsointendedformachine
learnerstofindoutaboutthepowerindustryandwhereitsneeds
are.Finally,itisaddressedtostudentsofeithersubjectorthegen-
eral public to demonstrate the challenges faced by an industry
that most are familiar with only through the power outlet on
the wall, and the solutions of these challenges by methods that
most are familiar with through online channels.
Incombiningmachinelearningsolutionswithpowerindustry
challenges,wefindthatitisoftennottechnologythatistheobsta-
clebutrathermanagerialtasks.Projectmanagementandchange
management are critical elements in the workflow that enable
machine learning methods to practically solve the problem. As
amachinelearner,itisimportanttopayattentiontotheseaspects
astheywilldecideoverthesuccessandfailureofaprojectortool.
Thisbookwillhaveserveditspurposeifapowercompanyuses
theadvicegivenhereinfacilitatingamachinelearningprojector
toolsettodrivevalue.Itismeantpartiallyasaninstructionman-
ual and partially as an inspiration for powercompany managers
xiii
xiv Foreword
who want to use machine learning and artificial intelligence to
improvethe industryand its efficiency.
Myheartfeltgratitudegoesouttothecoauthorsofthisbookfor
beingpartofthejourneyandcommunicatingtheirsuccessesand
lessons learned. Thank you to all those who have educated me
overtheyearsinthefieldsofpower,machinelearning,andman-
agement. Thank you to my wife and family for sparing me on
many an evening and weekend while writing this text. Finally,
thankyoutoyou,thereader,forpickingupthisbookandreading
it! Feedback is welcome and please feel freeto reachout.
1
Introduction
Patrick Bangerta,b
aArtificialIntelligence,SamsungSDSA,SanJose,CA,UnitedStates
bAlgorithmicaTechnologiesGmbH,BadNauheim,Germany
Chapter outline
1.1 Who thisbook is for 1
1.2 Preview ofthecontent 3
1.3 Power generation industryoverview 4
1.4 Fuels as limited resources 6
1.5 Challenges ofpowergeneration 8
References 11
1.1 Who this book is for
This book will provide an overview of the field of machine
learningasappliedtoindustrialdatasetsinthepowergeneration
industry. It will provide enough scientific knowledge for a man-
ager of a related project to understand what to look for and
howtointerprettheresults.Whilethisbookwillnotmakeyouinto
a machine learner, it will provide everything needed to talk suc-
cessfully with machine learners. It will also provide many useful
lessons learned in the management of such projects. As we will
learn, over 90% of the total effort put into these projects is not
mathematical in natureand allthese aspects will be covered.
A machinelearning project consists of four majorelements:
1. Management:Definingthetask,gatheringtheteam,obtaining
thebudget,assessingthebusinessvalue,andcoordinatingthe
othersteps in the procedure.
2. Modeling: Collecting data, describing the problem, doing the
scientific training of a model, and assessing that the model
is accurate and precise.
3. Deployment: Integrating the model with the other infrastruc-
turesothat it can be run continuously in realtime.
MachineLearningandDataScienceinthePowerGenerationIndustry.https://doi.org/10.1016/B978-0-12-819742-4.00001-9 1
#2021ElsevierInc.Allrightsreserved.
2 Chapter1 Introduction
4. Changemanagement:Persuadingthe end-users to take heed
ofthe new system and change their behavioraccordingly.
Mostbooksonindustrialdatasciencediscussmostlythefirst
item.Manybooksonmachinelearningdealonlywiththesecond
item. Itis however the whole process that is required to createa
success story. Indeed, the fourth step of change management is
frequentlythecriticalelement.Thisbookaimstodiscussallfour
parts.
Thebookaddressesthreemaingroupsofreaders:powerpro-
fessionals, machine learners and data scientists, and the general
public.
PowerprofessionalssuchasC-leveldirectors,plantmanagers,
andprocessengineerswilllearnwhatmachinelearningiscapable
of and what benefits may be expected. You will learn what is
neededtoreaptherewards.Thisbookwillprepareyouforadis-
cussionwithdatascientistssothatyouknowwhattolookforand
how to judge the results.
Machine learners and data scientists will learn about the
power industryand its complexities aswellasthe use cases that
theirmethodscanbeputtointhisindustry.Youwilllearnwhata
power professional expects to see from the technology and the
final outcome. The book will put into perspective some of the
issues that take center stage for data scientists, such as training
time and model accuracy, and relativize these to the needs of
the end-user.
For the general public, this book presents an overview of the
state of the art in applying a hyped field like machine learning
toabasicnecessitylikeelectricity.Youwilllearnhowbothfields
workandhowtheycanworktogethertosupplyelectricityreliably,
safely, and with less harmto the environment.
Oneofthemostfundamentalpoints,towhichweshallreturn
often, is that a practical machine learning project requires far
morethanjustmachinelearning.Itstartswithagoodqualitydata-
setandsomedomainknowledge,andproceedstosufficientfund-
ing, support, and most critically change management. All these
aspectswillbetreatedsothatyouobtainaholistic360-degreeview
ofwhatarealindustrialmachinelearningprojectlookslike.
The book can be divided into two parts. The first chapters
discussgeneralissuesofmachinelearningandrelevantmanage-
mentchallenges.Thesecondhalffocusesonpracticalcasestud-
iesthathavebeencarriedoutinrealindustrialplantsandreports
onwhathasbeendonealreadyaswellaswhatthefieldiscapable
of.Inthiscontext,thereaderwillbeabletojudgehowmuchofthe
marketingsurroundingmachinelearningishypeandhowmuch
is reality.
