Analysis of the German Power Market (with Python)
Along with France, Germany is one of the top energy producers in Central Europe. Its aggressive Energy Transition policy, known as the Energiewende, positioned the country as a renewable energy production leader in Europe. According to the Fraunhofer Institute for Solar Energy Systems ISE, in 2019, the share of renewables in Germany's net electricity generation reached 46 percent, up from 40.6 percent in 2018.
In this article, we will analyze data from the german power market using Python and its libraries. Our dataset shows daily information on the German power market from 07-Jul-2020 to 01-Oct-2020. It contains:
- Energy Consumption
- Wind + Solar Power Generation
- Other Sources Power Generation
- Electricity Spot Price
The information is provided in timeslices with a duration of 4 hours. According to this, each day is divided in 6 timeblocks. The timeslices are:
- From 00:00 to 04:00
- From 04:00 to 08:00
- From 08:00 to 12:00
- From 12:00 to 16:00
- From 16:00 to 20:00
- From 20:00 to 00:00
Data Preparation
As first step, we need to import the libraries we expect to use:
!pip install jovian
import jovian
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
Collecting jovian
Downloading jovian-0.2.20-py2.py3-none-any.whl (65 kB)
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Collecting uuid
Downloading uuid-1.30.tar.gz (5.8 kB)
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Building wheels for collected packages: uuid
Building wheel for uuid (setup.py) ... done
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Successfully built uuid
Installing collected packages: uuid, jovian
Successfully installed jovian-0.2.20 uuid-1.30