Data Analytics on Sustainable Energy and Mobility
Course Information:
| Course ID: | B-213 |
| Type of Course: | Elective |
| Semester: | Fall/Spring |
| ΕCTS: | 9 |
Instructors: Dr. Nikolaos Sifakis, PhD
Recommended Reading:
- Data Analytics: Paving the Way to Sustainable Urban Mobility. (2019). In E. G. Nathanail & I. D. Karakikes (Eds.), Advances in Intelligent Systems and Computing. Springer International Publishing. https://doi.org/10.1007/978-3-030-02305-8
- Data Science Applied to Sustainability Analysis. (2021). Elsevier. https://doi.org/10.1016/c2018-0-02415-9
- Computational Intelligent Data Analysis for Sustainable Development. (2016). Yu, T., Chawla, N., & Simoff, S. (Eds.). Chapman and Hall/CRC. https://doi.org/10.1201/b14799
- Sifakis, N., & Tsoutsos, T. (2021). Planning zero-emissions ports through the nearly zero energy port concept. In Journal of Cleaner Production (Vol. 286, p. 125448). Elsevier BV. https://doi.org/10.1016/j.jclepro.2020.125448
- Sifakis, N., Savvakis, N., Daras, T., & Tsoutsos, T. (2019). Analysis of the Energy Consumption Behavior of European RES Cooperative Members. In Energies (Vol. 12, Issue 6, p. 970). MDPI AG. https://doi.org/10.3390/en12060970
Eclass:
- Registration required
Course objectives:
The course aims at educating the Graduate students in the design of sustainable energy and mobility systems and at their assessment based on environmental, energy, and economic criteria. Special emphasis is put on the sustainable management of environmental impacts, monitoring and targeting systems, noise control, utilization of alternative fuels in transportation (biofuels, electricity), and the analysis of big energy data. Timeseries regarding energy demand and consumption are used to create dashboards of the most interesting findings.
Furthermore, renewable energy management and smart energy management systems (regional-local energy planning, sustainable management of natural resources) are presented; their role in sustainable development and energy transition is evaluated through data analytics methods and tools.
Lastly, the graduate students will be educated on how to forecast future trends by taking advantage of available big data.
Syllabus:
| Chapter 1: Introductory Material on Sustainable Development and Sustainable Mobility | |
| 1st Week: | Introduction (Data Analytics on Sustainable Development) |
| 2nd Week: | Advanced analysis and design of sustainable energy and mobility systems |
| 3rd Week: | Sustainable Transportation/Mobility Systems |
| 4th Week: | Hybrid Renewable Energy Systems |
| 5th Week: | Nearly Zero Energy Ports & Initial Project Allocation (Case Study & Data Acquisition Method) |
| Chapter 2: Introductory Material on Data Analytics | |
| 6th Week: | Introduction to simple Data Analytics Models and Tools using Python |
| 7th Week: | Introduction to advanced Data Analytics Models (AI/ML/ANN Models) |
| 8th Week: | Tutorial on how to use Data Analytics tools on Big Energy Data (AI Forecasting) |
| 9th Week: | Oral Presentation of the work progress for each student (20mins each) |
| 10th Week: | How to create the infrastructures of a sustainable future using online Databases |
| Chapter 3: Data Visualisation & Recap | |
| 11th Week: | Data Visualization Methods and Tools using Python |
| 12th Week: | Data Visualization Methods and Tools using the Tableau Software |
| 13th Week: | Meeting for answering the last questions and fixing problems regarding the projects |
| Final Presentations (Will be arranged for later during the exams) | |
Work Load:
Two projects per student including oral presentations.
Assessment method:
- 1st phase of the project (35%)
- 2nd phase of the project (40%)
- Oral Presentation of the project (25%)
Last modification: 24-02-2023
Technical University of Crete
Information
Contact
Technical University of Crete , Akrotiri,
Chania Crete, 73100,
Secretary Office,
Phone: +30 28210 37781 / 37788,
Email: secretariat@chenveng.tuc.gr, grammateia-chenveng@isc.tuc.gr
© Technical University of Crete 2018 |