# cqcn1991/Wind-Speed-Analysis

An elegant probability model for the joint distribution of wind speed and direction.
HTML Jupyter Notebook Other
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
data
docs
helpers
lib
output_HTML
.gitignore
GMM.ipynb
barebone_fitter.ipynb
data_formater.ipynb
import_file.py
notebook_looper.ipynb
playground.ipynb
requirements.txt

# A Probability Model for the Joint Distribution of Wind Speed and Direction (Gaussian Mixture Models, GMM)

## What is this?

This is a Python program for modeling the joint distribution of wind speed and direction.

The method is based on:

1. Harris, Cook, The parent wind speed distribution: Why Weibull?, http://www.sciencedirect.com/science/article/pii/S0167610514001056

2. Gaussian Mixture Models, http://scikit-learn.org/stable/modules/mixture.html

## Fitting Effect

Take Shanghai Hongqiao Intl as an example

### 1. PDF (Probability Density Function) Comparison

Left: Empirical PDF, Right: Model PDF

### 2. Speed and Direction Distribution Comparison

Histogram vs. Model

### 3. Sectoral Comparison

Left: Histogram vs. Model, Right: Empirical vs. Model CDF

### 4. Live Demo

The above results are available at https://cdn.rawgit.com/cqcn1991/Wind-Speed-Analysis/master/output_HTML/hongqiao_intl.html , along with other analysis.

## Applications

This model can be used to obtain the wind speed distribution, wind direction distribution and the joint distribution of wind speed and direction distribution with good accuracy.

Its potential applications include:

1. Wind energy evaluation, wind resource assessment (long term). For example, the optimization of wind farm layout.

2. Wind load analysis on structures, such as fatigue analysis. For example, the wind load on highrise buidings.

3. Pedestrian-level wind environment assessment.

## Gallery

Here is how the method applies to different places across the world. The Weibull distribution is listed as the traditional model for comparison.

### 4. Mid East

#### Al Maktoum Intl, United Arab Emirates

You can use the code toggle button and sidebar to navigate these reports.

## The Probability Model

The model I'm using is Gaussian Mixture Model, or simply put, the sum of some normal distributions. The idea is very simple:

#### 2. For specific location, its wind speed direction distribution is composed of several such wind characters, and hence the sum of several normal distributions:

fi is the PDF of normal distribution component, pi is its weight.

The chief beauty of this model is its elegance (simplicity). You can use such a simple model (GMM) to describe wind, contrary to previous Weibull combination and others.

For a complete explanation of how GMM fit the joint distribution of wind speed and direction, you can read

Harris, Cook, The parent wind speed distribution: Why Weibull?, http://www.sciencedirect.com/science/article/pii/S0167610514001056

And I'm also working on a paper about it, this is my Master Thesis. And if you have any interest (question, collaboration...) feel free to ask me. I open source the project is meant to help you try it.

## How to use it

### 1. Install Anaconda

This repo use Python 3, so you should use the 3 as well.

After installing Anaconda, there are still some additional packages you need to install:

Just run these in your command line to install them

``````pip install -r requirements.txt
``````

### 4. GMM Fitting

If you have any question, you could post it at issue, or mail me at 38306608#qq.com

### 5. R configuration (optional)

The program now uses some R packages to fit von Mises mixture model. You can either delete related code, or install R and Rpy 2 to enable this feature. Specifically:

### 6. Jupyter Extention config (Optional)

Install extentions from https://github.com/ipython-contrib/jupyter_contrib_nbextensions

``````pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user --skip-running-check
jupyter nbextension enable toc2/main
``````

## Acknowledgement

This work would be impossible without the help from Cook and Harris. Thank you!

You can’t perform that action at this time.