Course Video
Course Overview
Unlock the power of scientific programming to advance wind resource assessment. This course equips participants with practical skills in Python and its scientific ecosystem—including NumPy, pandas, xarray, and geopandas—as well as QGIS for geospatial data handling. You’ll learn to perform robust numerical wind resource assessments using specialised tools like Windkit and PyWAsP, gaining hands-on experience in processing, analysing, and visualising wind data.
Course Highlights
Transform your Python skills into wind energy expertise—master modern wind resource assessment with industry-leading tools and DTU scientists.
Ideal for programmers and engineers eager to bridge the gap between software development and renewable energy analytics, this course provides the tools and knowledge needed to contribute effectively to wind energy projects.
MAIN GOAL
By combining scientific Python programming with wind energy methodology, this course enables participants to perform robust, modern wind resource assessments and support real-world wind project development.
Learning Outcomes
After completion of this course, you will gain the ability to:
- + Develop and apply scientific programming techniques tailored to wind resource assessment.
- + Use Python and QGIS to process and interpret reanalysis, terrain, and wind measurement datasets.
- + Perform advanced numerical analyses with Windkit and PyWAsP to evaluate wind resources and support wind energy project development.
- + Build a comprehensive understanding of the methodologies, data sources, and best practices fundamental to modern wind resource assessment.
Meet Your Instructors
Senior Researcher
Senior Researcher
Admissions
Entry Requirements
- + Python skills: Ability to write functions and loops, familiarity with basic data structures (lists, dictionaries), experience with Jupyter notebooks.
- + Recommended Python knowledge: Basic numpy array operations, pandas DataFrames (reading CSV, filtering, grouping). If unfamiliar, complete a basic numpy/pandas tutorial before the course.
- + Statistics: Understanding of mean, standard deviation, distributions (especially Weibull), and basic regression concepts.
- + Wind energy: Basic familiarity with wind resource assessment concepts (what a wind climate is, what AEP means, why we model wakes).
- + Platform requirements (IMPORTANT): This course requires Windows 11 or Linux on x86 architecture. PyWAsP licensing does not support macOS or ARM processors natively.
Teaching and Assessment Methods
- + Live online sessions: 6 live sessions (two per week), approximately 1.5 hours each. All sessions are recorded. The live sessions focus on Q&A, demonstrations, and collaborative troubleshooting, while the core course content is delivered through self-paced materials.
- + Flexible time slots: Each live session is offered in two time slots to accommodate different time zones: morning (9:00 CET) and afternoon (15:00 CET), covering the same content.
- + Self-paced learning: Core content is provided through pre-recorded video lessons and prepared Jupyter notebooks, allowing participants to learn at their own pace between live sessions.
- + Workload: An expected total workload of 10–15 hours per week, including live sessions and self-study.
Application Deadline: TBC.
Practical Notes
- + Self-assessment:
- Python: Can you write a function that reads a CSV and computes the mean of a column? Can you index a pandas DataFrame by date range? If not, review the Python prerequisites below.
- Wind energy: Do you know what a Weibull distribution is? Can you explain why AEP estimates include uncertainty? If not, review the Wind Energy Fundamentals below. - + Python Scientific Stack (for wind energy professionals new to Python)
- NumPy basics: Arrays, broadcasting, computation. Review if unfamiliar with np.array, indexing, and vectorized operations.
- Pandas basics: Data tables, indexing, grouping, resampling. Review if unfamiliar with pd.DataFrame, groupby(), and time series indexing.
- Xarray basics (CRITICAL): Dimensions, coordinates, Datasets, DataArrays, subsetting, computation. Windkit is built entirely on xarray—if you are unfamiliar with xarray, this is your most important prerequisite. You should be comfortable with:
Warning: If you skip this prerequisite, Week 1 will be extremely difficult. Complete the Xarray Tutorial before Week 1. Budget 4-6 hours if xarray is new to you.
- Creating and indexing DataArrays and Datasets
- Using .sel() and .isel() for label-based and integer-based selection
- Understanding dimensions, coordinates, and attributes
- Basic operations like .mean(), .sum() along dimensions
- Loading and saving NetCDF files
- Plotting in Python: Matplotlib fundamentals, cartopy for map projections. Review if unfamiliar with plt.plot(), subplots, and basic cartographic visualization.
- Jupyter Notebooks: Notebook structure, markdown cells, code cells, running code, visualizing outputs. The course uses notebooks extensively.
Recommended resources
- NumPy Quickstart Tutorial
- Pandas Getting Started
- Matplotlib Tutorials - + Wind Energy Fundamentals (for programmers new to wind energy)
- How wind turbines work: Basic aerodynamics, power extraction, the Betz limit. Understand what a power curve represents.
- Wind resource assessment overview: Why we measure wind, what bankability means, the role of uncertainty in project finance.
- Wind statistics: The Weibull distribution and why it models wind speeds well. Wind roses and directional analysis.
- Wake effects: Why turbines in a wind farm produce less than isolated turbines. Basic wake physics.
- Key terminology: AEP (Annual Energy Production), capacity factor, hub height, met mast, lidar, mesoscale vs. microscale.
Recommended resources
- DTU Wind Energy E-Learning: Wind Energy Introduction (Coursera, free to audit)
- Wind Energy (Järvinen, M. et al.) - Wind energy fundamentals
- Global Wind Atlas - Explore to understand wind resource mapping
Fees & Funding
Tuition Fees
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Course Info
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