Wind Energy Modeling Bootcamp: Hands-on Python

Explore Betz law as the wind energy limit, then apply the Betz coefficient and generator efficiency to derive a practical power model and prepare a Python-based power-curve approach.

Learn to interpret wind turbine power curve, identify cutting wind speed, rated power, cut-out wind speed, and understand the power coefficient as the ratio of captured to available wind energy.

Learn to download wind speed data for any location using the data access viewer, choose hourly data at 50 meters, export CSV, and prepare it for Python modeling.

Google Colab provides an easy, online, no-install coding platform for this wind energy modeling bootcamp: hands-on Python, with preinstalled packages and no need to install anything with pip or conda.

Explore the Google Colab environment for wind energy modeling in Python, including creating and running notebooks, uploading and saving files, using code cells, and navigating shortcuts.

Develop a simple single turbine model in Python on Colab by importing pandas and matplotlib, loading wind speed data, computing wind power, and preparing the data frame for visualization.

Create a simple single turbine model for a 25 kilowatt wind turbine, filter hourly power output for cut-in, rated, and cut-out, and visualize the power curve with Python and pandas.

Visualize wind power data using the max plus db library to plot wind speed against power output, building a practical power curve with filtering and data concatenation.

Explore the Weibull wind speed model via its PDF, focusing on shape (K) and scale (C) parameters and use Python to visualize their impact on turbine selection.

Learn to visualize wind speed data using a Python Weibull distribution by importing pandas and numpy, computing the shape k and scale c, and generating a labeled probability distribution.

Visualize wind speed probabilities with a wind speed versus probability scatter plot, using colors to reveal a 2–4 m/s distribution that guides low cut-in turbine design.

Introduce a shift from a simple wind turbine power formula to a practical model. Incorporate sub models for temperature, variable air density, land type, and obstacles, with Python coding planned.

Explore the temperature sub-model with a linear gradient to estimate hub temperature from air temperature and height, and examine density methods (parametric, ideal gas, and interpolation extrapolation) via pressure gradients.

Apply the Hulman method to estimate wind speed at the hop height using friction factors. Include density correction and obstacle height to refine the wind model and power output.

Use Python and the wind power lib to model a single wind turbine, import model_chain and wind_turbine, and load Montreal weather data (wind speed, pressure, temperature) with pandas.

Introduce wind farm modeling by extending a practical wind turbine model with wake effects, lake effect, turbulence intensity, and smoothing to improve power output predictions and grid stability.

Develop a python wind farm model with wind power library, define a turbine fleet and farm data, apply wake losses and smoothing, and compute wind farm power output.

Visualize wind farm power output by comparing wind farm results to a single turbine, using Python models and sub models to show wake losses and turbulence intensity.

Learn how to set up Google Colab notebooks, rename and run cells, and use Python basics: variables, strings, booleans, and string methods like upper, find, and replace.

Master Python math operators, including plus, minus, asterisk for multiply, division, floor division with //, power with **, modulus for remainder, and commenting with the pound sign.

Learn how to assign values to variables using the assignment operator, including augmented assignment with +=, and apply basic math operations like multiply, divide, and remainder in Python.

Explore comparison operators to evaluate values and yield boolean results, as ten is greater than four, ten is less than four, and the equality sign differs from the assignment operator.

Demonstrates python's logical operators and, or, and not with a practical example (A = 20), showing how and requires both true, or needs one true, and not reverses the result.

Learn Python conditional statements using if, elif, and else with colon, indentation, and logical operators to implement age-based decision making and print outcomes.

Explore how to implement python loops using while conditions, variables, and augmented assignment to control iteration, prevent infinite loops, and generate sequential outputs.

Master lists in Python within wind energy modeling bootcamp: create and manipulate lists, indexing and slicing, and use insert, remove, and len to manage numbers and strings.

Create and manipulate dictionaries with keys and values using curly brackets, retrieve and update by key, use the value method, and delete items with del, featuring Montreal and London.

Explore tuples in Python by comparing them to lists and dictionaries, highlighting immutability, how to create them with parentheses, and why item assignments fail.

Master for loops in Python to iterate over sequences such as lists, dictionaries, and tuples. Practice with a list of city names, printing each element and using break when London.

Learn to use the range function in Python to create sequences from start to end-1, with an optional step, for hands-on exercises.