Design activity framework for visualization designDominika Mazur
An important aspect in visualization design is the connection between what a designer does and the decisions the designer makes. Existing design process models, however, do not explicitly link back to models for visualization design decisions. We bridge this gap by introducing the design activity framework, a process model that explicitly connects to the nested model, a well-known visualization design decision model. The framework includes four overlapping activities that characterize the design process, with each activity explicating outcomes related to the nested model. Additionally, we describe and characterize a list of exemplar methods and how they overlap among these activities. The design activity framework is the result of reflective discussions from a collaboration on a visualization redesign project, the details of which we describe to ground the framework in a real-world design process. Lastly, from this redesign project we provide several research outcomes in the domain of cybersecurity, including an extended data abstraction and rich opportunities for future visualization research.
Data Visualization & Design with School of DataSchool of Data
We all know data presentation (visualization) plays a large part in our School of Data workshops as a fundamental aspect of the data pipeline. But how do you know that, beyond using D3 or the latest dataviz app, you are helping people actually communicate visually?
The guest of this skillshare was Code for South Africa/School of Data Fellow, Hannah Williams
Schoolofdata.org
Okfn.org
http://code4sa.org/
Date: Thursday (Sept. 25, 2014)
www.hannahwilliams.co.za
hello@hannahwilliams.co.za
An overview of some methods and principles for big data visualization. The presentation quickly hits on the topic of dashboards and some cyber security uses. The topic of a big data lake is also briefly discussed in the context of a cyber security big data setup.
Power System Simulation: History, State of the Art, and ChallengesLuigi Vanfretti
This talk will give an overview of power system simulation technology through several decades, aiming to provide an understanding of the modeling philosophy and approach that has lead to the state of the art in (domain specific) power system simulation tools. This historical perspective will contrast the de facto proprietary software development method used by the power engineering community, against the open source development model. Aspects of resistance to change particular to the power system engineering community will be highlighted.
Given this particular context, power system simulation faces enormous challenges to adapt in order to satisfy simulation needs of both cyber-physical and sustainable system challenges. Such challenges will be highlighted during the talk.
There is, however, an opportunity for disruptive change in power system simulation technology emerging for the EU Smart Grid Mandate M/490, which requires "a set of consistent standards, which will support the information exchange (communication protocols and data models) and the integration of all users into the electric system operation." These regulatory aspects will be explained to highlight the importance of collaboration between the power system domain and computer system experts.
Open modeling and simulation standards may have a large role to play in the development of the European Smart Grid which will have to overcome challenges related to the design, operation and control of cyber-physical and sustainable electrical energy systems. To contribute to this role, the KTH SmarTS Lab research group has been applying the standardized Modelica language and the FMI standard for model exchange in order to couple the domain specific data exchange model (CIM) with the powerful and modern simulation technologies developed by the Modelica community. These efforts will be also discussed.
Design activity framework for visualization designDominika Mazur
An important aspect in visualization design is the connection between what a designer does and the decisions the designer makes. Existing design process models, however, do not explicitly link back to models for visualization design decisions. We bridge this gap by introducing the design activity framework, a process model that explicitly connects to the nested model, a well-known visualization design decision model. The framework includes four overlapping activities that characterize the design process, with each activity explicating outcomes related to the nested model. Additionally, we describe and characterize a list of exemplar methods and how they overlap among these activities. The design activity framework is the result of reflective discussions from a collaboration on a visualization redesign project, the details of which we describe to ground the framework in a real-world design process. Lastly, from this redesign project we provide several research outcomes in the domain of cybersecurity, including an extended data abstraction and rich opportunities for future visualization research.
Data Visualization & Design with School of DataSchool of Data
We all know data presentation (visualization) plays a large part in our School of Data workshops as a fundamental aspect of the data pipeline. But how do you know that, beyond using D3 or the latest dataviz app, you are helping people actually communicate visually?
The guest of this skillshare was Code for South Africa/School of Data Fellow, Hannah Williams
Schoolofdata.org
Okfn.org
http://code4sa.org/
Date: Thursday (Sept. 25, 2014)
www.hannahwilliams.co.za
hello@hannahwilliams.co.za
An overview of some methods and principles for big data visualization. The presentation quickly hits on the topic of dashboards and some cyber security uses. The topic of a big data lake is also briefly discussed in the context of a cyber security big data setup.
Power System Simulation: History, State of the Art, and ChallengesLuigi Vanfretti
This talk will give an overview of power system simulation technology through several decades, aiming to provide an understanding of the modeling philosophy and approach that has lead to the state of the art in (domain specific) power system simulation tools. This historical perspective will contrast the de facto proprietary software development method used by the power engineering community, against the open source development model. Aspects of resistance to change particular to the power system engineering community will be highlighted.
Given this particular context, power system simulation faces enormous challenges to adapt in order to satisfy simulation needs of both cyber-physical and sustainable system challenges. Such challenges will be highlighted during the talk.
There is, however, an opportunity for disruptive change in power system simulation technology emerging for the EU Smart Grid Mandate M/490, which requires "a set of consistent standards, which will support the information exchange (communication protocols and data models) and the integration of all users into the electric system operation." These regulatory aspects will be explained to highlight the importance of collaboration between the power system domain and computer system experts.
Open modeling and simulation standards may have a large role to play in the development of the European Smart Grid which will have to overcome challenges related to the design, operation and control of cyber-physical and sustainable electrical energy systems. To contribute to this role, the KTH SmarTS Lab research group has been applying the standardized Modelica language and the FMI standard for model exchange in order to couple the domain specific data exchange model (CIM) with the powerful and modern simulation technologies developed by the Modelica community. These efforts will be also discussed.
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and Gr...Luigi Vanfretti
Title:
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and GridDyn
Presenters:
Luigi Vanfretti (RPI) & Philip Top (LNLL)
luigi.vanfretti@gmail.com, top1@llnl.gov
Abstract:
The Modelica language, being standardized and equation-based, has proven valuable for the for model exchange, simulation and even for model validation applications in actual power systems. These important features have been now recognized by the European Network of Transmission System Operators, which have adopted the Modelica language for dynamic model exchange in the Common Grid Model Exchange Standard (v2.5, Annex F).
Following previous FP7 project results, within the ITEA 3 openCPS project, the presenters have continued the efforts of using the Modelica language for power system modeling and simulation, by developing and maintaining the OpenIPSL library: https://github.com/SmarTS-Lab/OpenIPSL
This seminar first gives an overview of the origins of the OpenIPSL and it’s models, it contrasts it against typical power system tools, and gives an introduction the OpenIPSL library. The new project features that help in the OpenIPSL maintenance (use of continuous integration, regression testing, documentation, etc.) are also described.
Finally, the seminar will present current work at LNLL that exploits OpenIPSL in coordination with other tools including ongoing work integrating openIPSL models into GridDyn an open-source power system simulation tool, as well as a demos of the use of openIPSL libraries in GridDyn.
Bios:
Luigi Vanfretti (SMIEEE’14) obtained the M.Sc. and Ph.D. degrees in electric power engineering at Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.
He was with KTH Royal Institute of Technology, Stockholm, Sweden, as Assistant 2010-2013), and Associate Professor (Tenured) and Docent (2013-2017/August); where he lead the SmarTS Lab and research group. He also worked at Statnett SF, the Norwegian electric power transmission system operator, as consultant (2011 - 2012), and Special Advisor in R&D (2013 - 2016).
He joined Rensselaer Polytechnic Institute in August 2017, to continue to develop his research at ALSETLab: http://alsetlab.com
His research interests are in the area of synchrophasor technology applications; and cyber-physical power system modeling, simulation, stability and control.
Philp Top (Lawrence Livermore National Lab)
PhD 2007 Purdue University. Currently a Research Engineer at Lawrence Livermore National Laboratory in Livermore, CA. Philip has been involved in several projects connected with the DOE effort on Grid Modernization including projects on modeling and simulation, co-simulation and smart grid data analytics. He is the principle developer on the open source power system simulation tool GridDyn, and a key contributor to the HELICS open source co-simulation framework.
introduction to modeling, Types of Models, Classification of mathematical mod...Waqas Afzal
Types of Systems
Ways to study system
Model
Types of Models
Why Mathematical Model
Classification of mathematical models
Black box, white box, Gray box
Lumped systems
Dynamic Systems
Simulation
Em computação quântica, um algoritmo quântico é um algoritmo que funciona em um modelo realístico de computação quântica. O modelo mais utilizado é o modelo do circuito de computação quântica.
Dr. Roitman discusses the use of Artificial Intelligence to solve complex and insoluble problems. Artificial intelligence approach is in the root of I Know First predictive algorithm.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Reactive Programming, Traits and Principles. What is Reactive, where does it come from, and what is it good for? How does it differ from event driven programming? It only functional?
In this deck from the Argonne Training Program on Extreme-Scale Computing 2019, Jonathan Baker from the University of Chicago presents: Quantum Computing: The Why and How.
"Jonathan Baker is a second year Ph.D student at The University of Chicago advised by Fred Chong. He is studying quantum architectures, specifically how to map quantum algorithms more efficiently to near term devices. Additionally, he is interested in multivalued logic and taking advantage of quantum computing’s natural access to higher order states and using these states to make computation more efficient. Prior to beginning his Ph.D., he studied at the University of Notre Dame where he obtained a B.S. of Engineering in computer science and a B.S. in Chemistry and Mathematics."
Watch the video: https://wp.me/p3RLHQ-l1i
Learn more: https://extremecomputingtraining.anl.gov/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Wanted!: Open M&S Standards and Technologies for the Smart Grid - Introducing...Luigi Vanfretti
Title:
Wanted! - Open M&S Standards and Technologies for the Smart Grid
Subtitle:
Introducing the Open Source iTesla Power Systems Modelica Library and the RaPId Toolbox for Model Identification and Validation
Abstract:
Modeling and Simulation (M&S) technologies have a broad set of applications in power systems, from infrastructure planning, through real-time testing of components, and even for training operators to use decision support systems. However, power system M&S technologies face a great challenge to meet when designing, testing, operating and controlling cyber-physical and sustainable electrical energy systems and components, a.k.a “Smart Grids”.
The speaker claims that open M&S standards can have a large role to play in the development of Smart Grids. This claim will be justified with three examples.
The first example describes the experience gained during the EU FP7 iTesla project where the iTesla Power Systems Modelica Library (iPSL) was designed using the Modelica language. The Modelica language, being standardized and equation-based, has proven valuable for the project for model exchange, and even simulation of actual power networks.
Within the iTesla project, the KTH SmarTS Lab research group has been also applying the FMI standard for model exchange in order to develop a software prototype called RaPId. The RaPId Toolbox aims to provide a “virtual laboratory” to solve parameter identification and model validation problems for any kind of model represented in an FMU, but specifically, for power systems.
The third example comes from a collaboration with Xogeny. It will be shown how it is possible to exploit the FMI to decouple the model from the simulator tool, and thus, exploit the model in unforeseen ways. This shows that is possible develop customized and stand-alone analysis tools using web technologies, giving analyst more time for “analysis”. This approach has an enormous potential for typical analysis applications, but even more, for education.
Mining Big Data Streams with APACHE SAMOAAlbert Bifet
In this talk, we present Apache SAMOA, an open-source platform for
mining big data streams with Apache Flink, Storm and Samza. Real time analytics is
becoming the fastest and most efficient way to obtain useful knowledge
from what is happening now, allowing organizations to react quickly
when problems appear or to detect new trends helping to improve their
performance. Apache SAMOA includes algorithms for the most common
machine learning tasks such as classification and clustering. It
provides a pluggable architecture that allows it to run on Apache
Flink, but also with other several distributed stream processing
engines such as Storm and Samza.
Talk of Ali Mousavi "Event-Modelling An Engineering Solution for Control and Analysis of Complex Systems" at 116th regular meeting of INCOSE Russian chapter, 14-Sep-2016
Jogging While Driving, and Other Software Engineering Research Problems (invi...David Rosenblum
invited talk presented for the Distinguished Lecturer Series of the Department of Computer Science at the University of Illinois at Chicago, 10 April 2014
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and Gr...Luigi Vanfretti
Title:
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and GridDyn
Presenters:
Luigi Vanfretti (RPI) & Philip Top (LNLL)
luigi.vanfretti@gmail.com, top1@llnl.gov
Abstract:
The Modelica language, being standardized and equation-based, has proven valuable for the for model exchange, simulation and even for model validation applications in actual power systems. These important features have been now recognized by the European Network of Transmission System Operators, which have adopted the Modelica language for dynamic model exchange in the Common Grid Model Exchange Standard (v2.5, Annex F).
Following previous FP7 project results, within the ITEA 3 openCPS project, the presenters have continued the efforts of using the Modelica language for power system modeling and simulation, by developing and maintaining the OpenIPSL library: https://github.com/SmarTS-Lab/OpenIPSL
This seminar first gives an overview of the origins of the OpenIPSL and it’s models, it contrasts it against typical power system tools, and gives an introduction the OpenIPSL library. The new project features that help in the OpenIPSL maintenance (use of continuous integration, regression testing, documentation, etc.) are also described.
Finally, the seminar will present current work at LNLL that exploits OpenIPSL in coordination with other tools including ongoing work integrating openIPSL models into GridDyn an open-source power system simulation tool, as well as a demos of the use of openIPSL libraries in GridDyn.
Bios:
Luigi Vanfretti (SMIEEE’14) obtained the M.Sc. and Ph.D. degrees in electric power engineering at Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.
He was with KTH Royal Institute of Technology, Stockholm, Sweden, as Assistant 2010-2013), and Associate Professor (Tenured) and Docent (2013-2017/August); where he lead the SmarTS Lab and research group. He also worked at Statnett SF, the Norwegian electric power transmission system operator, as consultant (2011 - 2012), and Special Advisor in R&D (2013 - 2016).
He joined Rensselaer Polytechnic Institute in August 2017, to continue to develop his research at ALSETLab: http://alsetlab.com
His research interests are in the area of synchrophasor technology applications; and cyber-physical power system modeling, simulation, stability and control.
Philp Top (Lawrence Livermore National Lab)
PhD 2007 Purdue University. Currently a Research Engineer at Lawrence Livermore National Laboratory in Livermore, CA. Philip has been involved in several projects connected with the DOE effort on Grid Modernization including projects on modeling and simulation, co-simulation and smart grid data analytics. He is the principle developer on the open source power system simulation tool GridDyn, and a key contributor to the HELICS open source co-simulation framework.
introduction to modeling, Types of Models, Classification of mathematical mod...Waqas Afzal
Types of Systems
Ways to study system
Model
Types of Models
Why Mathematical Model
Classification of mathematical models
Black box, white box, Gray box
Lumped systems
Dynamic Systems
Simulation
Em computação quântica, um algoritmo quântico é um algoritmo que funciona em um modelo realístico de computação quântica. O modelo mais utilizado é o modelo do circuito de computação quântica.
Dr. Roitman discusses the use of Artificial Intelligence to solve complex and insoluble problems. Artificial intelligence approach is in the root of I Know First predictive algorithm.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Reactive Programming, Traits and Principles. What is Reactive, where does it come from, and what is it good for? How does it differ from event driven programming? It only functional?
In this deck from the Argonne Training Program on Extreme-Scale Computing 2019, Jonathan Baker from the University of Chicago presents: Quantum Computing: The Why and How.
"Jonathan Baker is a second year Ph.D student at The University of Chicago advised by Fred Chong. He is studying quantum architectures, specifically how to map quantum algorithms more efficiently to near term devices. Additionally, he is interested in multivalued logic and taking advantage of quantum computing’s natural access to higher order states and using these states to make computation more efficient. Prior to beginning his Ph.D., he studied at the University of Notre Dame where he obtained a B.S. of Engineering in computer science and a B.S. in Chemistry and Mathematics."
Watch the video: https://wp.me/p3RLHQ-l1i
Learn more: https://extremecomputingtraining.anl.gov/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Wanted!: Open M&S Standards and Technologies for the Smart Grid - Introducing...Luigi Vanfretti
Title:
Wanted! - Open M&S Standards and Technologies for the Smart Grid
Subtitle:
Introducing the Open Source iTesla Power Systems Modelica Library and the RaPId Toolbox for Model Identification and Validation
Abstract:
Modeling and Simulation (M&S) technologies have a broad set of applications in power systems, from infrastructure planning, through real-time testing of components, and even for training operators to use decision support systems. However, power system M&S technologies face a great challenge to meet when designing, testing, operating and controlling cyber-physical and sustainable electrical energy systems and components, a.k.a “Smart Grids”.
The speaker claims that open M&S standards can have a large role to play in the development of Smart Grids. This claim will be justified with three examples.
The first example describes the experience gained during the EU FP7 iTesla project where the iTesla Power Systems Modelica Library (iPSL) was designed using the Modelica language. The Modelica language, being standardized and equation-based, has proven valuable for the project for model exchange, and even simulation of actual power networks.
Within the iTesla project, the KTH SmarTS Lab research group has been also applying the FMI standard for model exchange in order to develop a software prototype called RaPId. The RaPId Toolbox aims to provide a “virtual laboratory” to solve parameter identification and model validation problems for any kind of model represented in an FMU, but specifically, for power systems.
The third example comes from a collaboration with Xogeny. It will be shown how it is possible to exploit the FMI to decouple the model from the simulator tool, and thus, exploit the model in unforeseen ways. This shows that is possible develop customized and stand-alone analysis tools using web technologies, giving analyst more time for “analysis”. This approach has an enormous potential for typical analysis applications, but even more, for education.
Mining Big Data Streams with APACHE SAMOAAlbert Bifet
In this talk, we present Apache SAMOA, an open-source platform for
mining big data streams with Apache Flink, Storm and Samza. Real time analytics is
becoming the fastest and most efficient way to obtain useful knowledge
from what is happening now, allowing organizations to react quickly
when problems appear or to detect new trends helping to improve their
performance. Apache SAMOA includes algorithms for the most common
machine learning tasks such as classification and clustering. It
provides a pluggable architecture that allows it to run on Apache
Flink, but also with other several distributed stream processing
engines such as Storm and Samza.
Talk of Ali Mousavi "Event-Modelling An Engineering Solution for Control and Analysis of Complex Systems" at 116th regular meeting of INCOSE Russian chapter, 14-Sep-2016
Jogging While Driving, and Other Software Engineering Research Problems (invi...David Rosenblum
invited talk presented for the Distinguished Lecturer Series of the Department of Computer Science at the University of Illinois at Chicago, 10 April 2014
Jogging While Driving, and Other Software Engineering Research Problems (invi...
GMMIW_Grp1_Final
1. The Problem Stochastic Process Simulations
Stochastic Modelling
for Hydro-Electric Reservoir Management
Team 1
Pacific Institute of Mathematical Sciences
Graduate Mathematical Modelling in Industry Workshop - 2016
August 13, 2016
2. The Problem Stochastic Process Simulations
Our Group!
• Ismail Hossain - University of Manitoba
• Clifford Allotey - University of Manitoba
• Farzaneh Jannat - University of Manitoba
• Weifei Ouyang - Shanghai Jiao Tong University
• Alfred Liu - University of Windsor
• Clint Seinen - University of Victoria
• Faisal Atakora - University of Manitoba
3. The Problem Stochastic Process Simulations
Our Group!
• Ismail Hossain - University of Manitoba
• Clifford Allotey - University of Manitoba
• Farzaneh Jannat - University of Manitoba
• Weifei Ouyang - Shanghai Jiao Tong University
• Alfred Liu - University of Windsor
• Clint Seinen - University of Victoria
• Faisal Atakora - University of Manitoba
Mentor: Dr. Fabian Bastin - University of Montreal
6. The Problem Stochastic Process Simulations
Introduction to Inflow Modeling
“The central element is a stochastic model for natural inflows”
(Pritchard, 2014)
Major complications
• How to model the seasonality effects?
• How to model the spatial and time correlations?
7. The Problem Stochastic Process Simulations
Inflows usually have positive serial relation.The model takes the
form:
Xt = Ft(Wt−1)
• Wt−1 is state variable at time t − 1, for univariate, we always
take Wt−1 = Xt−1,
for higher-order model take Wt−1 = (Xt−1, Xt−2, . . . , Xt−r )
Existing models
• Multivariate AR(1)
• Iterated function system
No current approach is totally satisfactory.
9. The Problem Stochastic Process Simulations
4-Dam System!
Instead of all the dams in Quebec, we consider the following four
dam system
10. The Problem Stochastic Process Simulations
How We Attacked the Problem
• Analyzed the historical data
• to determine the extent of correlations and create foundation
for the stochastic process
11. The Problem Stochastic Process Simulations
How We Attacked the Problem
• Analyzed the historical data
• to determine the extent of correlations and create foundation
for the stochastic process
• formulate a stochastic process
12. The Problem Stochastic Process Simulations
How We Attacked the Problem
• Analyzed the historical data
• to determine the extent of correlations and create foundation
for the stochastic process
• formulate a stochastic process
• implement a simulation of the 4-dam system
13. The Problem Stochastic Process Simulations
Stochastic Model of Inflow
Xt − µt = ϕ(Xt−1 − µt−1) + εt
where,
• Xt : Inflow of water at time t (weeks)
• µt : Expected value of inflow at t
• ϕ : Auto-regression parameter (ϕ = -0.4589)
• Xt−1 : Inflow of water at time t − 1
• µt−1 : Expected value of inflow at time t − 1
• εt : Error term of week t that follows the standard normal
distribution
14. The Problem Stochastic Process Simulations
Let Xit be the inflows into the respective dams (i = 1, 2, 3, 4.)
X1t
X2t
X3t
X4t
∼ N(µt, Σt)
where Σt is covariance matrix at time t. With this setup the
spatial correlation in the data set will be reflected in the
simulation. However the temporal correlation is not guaranteed,
thus we incorporate the AR(1) model into our model.
22. The Problem Stochastic Process Simulations
Simulations
• SimJulia! Why?
• Good for discrete events
• Allows room for future complexities
• Potential for random “complications”, or breakdowns
23. The Problem Stochastic Process Simulations
Simulations
Prior to a realistic model containing ALL our lovely
components......
24. The Problem Stochastic Process Simulations
Simulations
Prior to a realistic model containing ALL our lovely
components......we prototype!
• start with an initial volume
• wait a week
• make a random decision on what to spill/store/run through
turbines
• update system with decision and random inflows according to:
Volnew = Volprev + inflows + Volupstream − Volturbine − Volspill
25. The Problem Stochastic Process Simulations
Simulations
Prior to a realistic model containing ALL our lovely
components...... we prototype!
26. The Problem Stochastic Process Simulations
Simulations
The Real Model!
Again, we start with an initial volume behind the reservoirs.
27. The Problem Stochastic Process Simulations
Simulations
The Real Model!
Again, we start with an initial volume behind the reservoirs.
Then create the scenario tree with 200 realizations of our
stochastic process.
28. The Problem Stochastic Process Simulations
Simulations
The scenario tree is then input into the C++ optimizer, which
then decides on how much water to run through the turbines or
how much is spilled.
29. The Problem Stochastic Process Simulations
Simulations
The scenario tree is then input into the C++ optimizer, which
then decides on how much water to run through the turbines or
how much is spilled.
Unfortunately.....
31. The Problem Stochastic Process Simulations
Simulations
But!
We know SimJulia can be used!
32. The Problem Stochastic Process Simulations
Simulations
But!
We know SimJulia can be used!
We know our stochastic method can be used to create the
necessary scenario tree format!
33. The Problem Stochastic Process Simulations
Simulations
But!
We know SimJulia can be used!
We know our stochastic method can be used to create the
necessary scenario tree format!
We know scenred2 and the C++ optimizer can be called from
with-in the SimJulia environment!
34. The Problem Stochastic Process Simulations
Simulations
But!
We know SimJulia can be used!
We know our stochastic method can be used to create the
necessary scenario tree format!
We know scenred2 and the C++ optimizer can be called from
with-in the SimJulia environment!
We have created a solid frame-work for future work on this project!