Wind Energy in Europe and Data Science

Europe is the oldest market of wind energy in the world and it finished 2019 with more than 204 GW of installed capacity distributed in 39 countries, according to the report “Wind Energy in Europe in 2019” from windeurope.org. Germany is the leader of the market with 61.3 GW of total installed capacity, from which 7.4 GW are offshore and 53.9 are onshore wind plants.

In 2019, the onshore wind plants in Europe added a total of 11.7 GW and the offshore 3.6 GW, with a total of 15.4 GW of new installed capacity. The country with the highest contribution is United Kingdom with 629 MW onshore and 1,764 MW offshore, representing 16% of the total.

Germany was the 3rd country with 1,078 MW onshore and 1,111 MW offshore, with 14% of the total. Spain installed the largest capacity of onshore wind plants in Europe with 2,319 MW and 15% of the total. Sweden is the 4th country in Europe with 10% of the new installations in 2019, all onshore plants.

This report is useful to understand how is the wind energy industry in European countries in the last year. Countries with highest new installation capacity had more services related to engineering projects, material manufacturing, civil works, environmental analysis and any other related works, and it is an indicative for the following years.

One part of my work is to analyse data like this. The industry is full of decision makers who need good insights to decide where to make investments and how to find business opportunities, so a good data visualization is essential to a professional in this sector. This is one part of the called “Data Science” and I would like to share some of the things that I am learning recently.

Data science is one of the trend topics for 2020 and it can be interesting to use it for the wind industry analysis. The demand for this type of professional is increasing for market analysis, wind plants operation, financial sector and so on.

The programming language that I use is Python because is the most known for data science. There areplenty of IDEs (Integrated Development Environment)to use in data science so I will not focus on it. I like to use Jupyter Notebook with Anaconda distribution because it is simple and easy to use. For the combination with other scripts I use the text editor Atom which is light and has a beautiful interface.

The next graph shows the new installations of wind plants in Europe in 2019, showing the gross installations in MW by country, with a division between onshore and offshore plants. This graph was made using Pandas to manipulate the data and Matplotlib to crate the graph.

For those who know Python language, this plot is simple. The “tricks” that we need to know for this plot are: two data values per country, rotate the labels from X axis and remove the top and right axis lines.

First let’s take a look at the bar chart. To plot two data values, one on top and the other in the bottom, you need to assign the bottom value in the line where you are plotting the top value (line 25). To rotate the X label sticks, you just need to use “rotate = n” where n is the angle that you want to rotate (line 27) and to remove the top and right axis you can use the subclass ‘spines’ from matplotlib (lines 29 and 30). The next image show the code that I used for this plot in Atom text editor.

Now for the pie chart, or ‘donut chart’, we need to work with parameters such as autopct, textprops, pct distance and start angle (lines 56 and 57). In order to set white color for the percentage inside the chart we create a for loop (lines 62 and 63). To give the donut aspect for the chart, we need to create a white pie chart after the first chart (remember that in Python, the order of the code matters) and finally we make an annotation of the total new wind capacity of 15.4 GW at the center of the graph.

For this first case I created 03 dictionaries: one for onshore, other for offshore and the last for the share. Python is an easy programming language and even if you never used it, by reading the code you can understand the logic and how it was created. On the next publications I will start to work with CSV or EXCEL files so we can start to play with larger datasets.

Thank you for the reading! If you have any question or suggestion leave a comment or send a message, it will be my pleasure to answer it: afonso.lugo@epowerbay.com.

Take a look at the publications about wind energy in extreme cold conditions:

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