2024 | Big Data in Energy: a practical introduction

Course description and aims

In this course, you will learn to apply the methods of data science and AI analysis to data pertaining to renewable energy technologies, including multidimensional data from renewable energy systems containing energy conversion and storage devices, and materials informatics data. The course will will explain why big data science is useful in energy science, and various types of data processing/analyzing methods. Furthermore, the course will introduce examples of application of data-scientific analyses to the development of materials (materials informatics) and informatics of energy systems for a carbon-free society. The course includes following topics using real energy data collected in Tokyo Tech (via the proprietary “Ene-Swallow” system):

1) Perspective of energy technologies towards a carbon-free society (present and future): Brief overview of traditional energy and storage technologies and novel, more sustainable technologies. Highlights an increased role of data with the new technologies.
2) Key data analysis techniques including selected machine learning techniques used in data science and AI analysis relating to energy.
3) Functional materials as key to the development of advanced energy conversion and storage technologies. Informatics aspects of materials development.
4) Utilization of big data in grid cooperated/distributed energy system towards a carbon-free society (the energy data of Tokyo Tech EEI (Environmental Energy Innovation) building, the data from electric vehicles etc.)

The student will work with real data from the “Ene-Swallow” system in Tokyo Tech. The students will perform simulations on their own computers connected to the campus network. The course is made suitable even for the student not familiar with programming.
The course enrolment is limited to 40 students, with priority given to the students belonging to Tokyo Tech Academy of Energy and Informatics.

Student learning outcomes

Students will be able to understand and apply data analysis processes to solution of problems of energy informatics, understand and use machine learning tools (methods and software) and procedures useful in the analysis of data generated by energy technologies. Students will also learn the basics of key energy generation technologies and their interactions and of related functional materials.

Keywords

Energy system, Renewable energy, energy storage, data analysis, big data, machine learning

Competencies that will be developed

✔ Specialist skills	Intercultural skills	Communication skills	✔ Critical thinking skills	✔ Practical and/or problem-solving skills
✔ Specialist skills, Practical and/or problem-solving skills

Class flow

Each class lasts about 100 min. Classes will feature lectures and exercise periods. Some of the lectures will be given as master-classes (i.e. with students repeating on their computers the procedures performed by the professor on their computer) and some will be delivered traditionally. Exercises will involve data analysis using methods and tools introduced in the lectures. Each class will have a brief quiz to provide constant feedback to the professor and to the student about the degree of absorption of the material and to reveal any need for review or adjustments.

Course schedule/Required learning

	Course schedule	Required learning
Class 1	Energy system transformation toward carbon neutrality and overview of this course(Prof. Ihara): What are the technologies required for carbon neutrality, and how is big data science used in energy technologies (the focus of Tokyo Tech academy of Energy and Informatics)? In addition, we will also explain the importance of 1. optimization and advanced control of energy systems using big data, 2. the concept of energy materials informatics, which are informatics unique to energy materials which is also dealt with in this course. The outline of the entire course will also be given in this lecture.	The students will understand energy-related technologies for carbon neutrality and the growing importance of data. Additionally, the students will understand the outline of grid cooperated real time energy systems and importance of developing new energy materials.
Class 2	Key data analysis techniques and introduction to selected machine learning techniques --knowledge basis of informatics-- lecture (Assoc. Prof. Manzhos & Prof. Ihara): Recapitulation of linear regression methods, correlation analysis, and PCA. Neural networks and deep learning. Kernel methods on the example of Gaussian process regression (GPR). Linear and nonlinear dimensionality reduction. Clustering. Pros and cons of all these techniques. Suitability for big data.	Students are expected to have been exposed to linear methods - this material will be briefly refreshed. The students will learn practical basics of neural networks including deep networks as well as the basics of kernel methods such as GPR and their pros and cons. The advantages of non-linear analysis in multidimensional spaces of features will be highlighted, including those of nonlinear dimensionality reduction and clustering.
Class 3	Key data analysis techniques and introduction to selected machine learning techniques --Exercise--(Assoc. Prof. Manzhos & Prof. Ihara): Exercise 1: Students learn invocation in the Python environment of techniques covered in the lecture.	Students will practically use the basic knowledge on data science gained in the lecture, and how to use the basic methods of data analysis. Acquiring the practical ability to apply the basic methods of data science that you learned in class 2 to your own energy research.
Class 4	Functional materials as a key to the development of advanced generation and storage technologies. --Introducing new functional energy materials requiring large scale installation of renewable energy--lecture (Assoc. Prof. Manzhos & Prof. Ihara): Introduction to the materials aspect of renewables. Types of functional materials and interfaces used in renewable energy technologies: Electronic materials, intercalation materials, catalysts, nanomaterials and low-dimensional materials and interfaces. Key phenomena responsible for core functionality, including light absorption, electron and ion transport and key reactions. Informatics aspects of materials development. Mapping between descriptors and desired properties. Choices of descriptors. Examples from the design of fuel cell catalysts and solar cell materials.	The students will familiarize themselves with kinds of data encountered in materials informatics and how they can be used for rational design of functional materials. Students will understand the limits of simple linear regression based analysis, and the need for more involved and in particular machine learning based techniques.
Class 5	Functional materials as a key to the development of advanced energy conversion and storage technologies. --Exercise--(Assoc. Prof. Manzhos & Prof. Ihara): Exercise 2: Students learn how to source data from the web, analyze them statistically and preprocess for further analysis. Furthermore, students will work with a provided dataset containing data on composition and performance of catalytic materials and will try to machine-learn performance characteristics from the composition.	Through practical exercises, the students will understand the limitations of simple linear regression and the need for more complex methods, especially those based on machine learning, and acquire the practical skills to apply them to their own energy researches.
Class 6	Energy meets big data in advanced energy system 1 --Analysis of EV data--(Prof. Ihara & Assoc.Prof. Manzhos): Introduction of present ICT structure of “Ene-Swallow”. Exercise 3: Students will acquire Tokyo Tech EEI building data and electric vehicle (EV) data from the cloud data server of “Ene-Swallow” and the connected EVs, including multiple types of data on a multitude of solar cells, fuel cell, gas engines, butteries as well as of energy consuming devices and location information of EVs. Furthermore, the students will analyze the location information and driving information of EVs. Finally, the students will consider the analyzed results based on energy science.	The students will experience data generated in a grid cooperated energy system “Ene-Swallow”. The students will also understand examples of hybrid considerations of data science analysis and energy science, using electric vehicle operational data.
Class 7	Energy meets big data in advanced energy system 2 --Analysis of building energy big data--(Prof. Ihara & Assoc.Prof. Manzhos): Exercise 4: The students will analyze the data from the EEI building acquired in class 6, which contain high-dimensional unlabeled data and a small number of labeled data. The students will try to identify collective variables by using information based on energy science as well as data analysis.	The students will experience work with very high dimensional data. They will learn to identify the meaning of the data from data statistics. They will apply different statistical and clustering techniques to identify a tractable number of underlying variables. The students will finally understand the outline and the importance of big data science in energy technologies.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

None

Reference books, course materials, etc.

Chapters from:
Fundamentals of Materials for Energy and Environmental Sustainability, Eds. D. S. Ginley, D. Cahen, Cambridge University Press, 2011, ISBN:9781107000230
A. Vieira da Rosa, Fundamentals of Renewable Energy Processes (Third Edition), Academic Press / Elsevier, 2013, ISBN: 978-0-12-397219-4
Articles from research literature.
Reference books, agency reports, and original course materials.

Assessment criteria and methods

Students' understanding will be assessed by exercises and reports.

Related courses

ENR.A401 ： Interdisciplinary scientific principles of energy 1
ENR.A403 ： Interdisciplinary principles of energy devices 1
ENR.A404 ： Interdisciplinary principles of energy devices 2
ENR.A405 ： Interdisciplinary Energy Materials Science 1
ENR.A406 ： Interdisciplinary Energy Materials Science 2
ENR.A407 ： Energy system theory
ENR.B431 ： Recent technologies of fuel cells, solar cells butteries and energy system

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).

Manabu Ihara, mihara[at]chemeng.titech.ac.jp
Sergei Manzhos, Manzhos.s.aa[at]m.titech.ac.jp

TOKYO INSTITUTE OF TECHNOLOGY