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Chapter with code

import pandas as pd
import matplotlib.pyplot as plt

Prepare data

See also:

All the commands used here are described in the pandas documentation

First load a dataset from github:

data = pd.read_csv('https://github.com/jenfly/opsd/raw/master/opsd_germany_daily.csv')

And take a look:

data
Date Consumption Wind Solar Wind+Solar
0 2006-01-01 1069.18400 NaN NaN NaN
1 2006-01-02 1380.52100 NaN NaN NaN
2 2006-01-03 1442.53300 NaN NaN NaN
3 2006-01-04 1457.21700 NaN NaN NaN
4 2006-01-05 1477.13100 NaN NaN NaN
... ... ... ... ... ...
4378 2017-12-27 1263.94091 394.507 16.530 411.037
4379 2017-12-28 1299.86398 506.424 14.162 520.586
4380 2017-12-29 1295.08753 584.277 29.854 614.131
4381 2017-12-30 1215.44897 721.247 7.467 728.714
4382 2017-12-31 1107.11488 721.176 19.980 741.156

4383 rows × 5 columns

data.dtypes

Date            object
Consumption    float64
Wind           float64
Solar          float64
Wind+Solar     float64
dtype: object

Let’s make the Date column to a datetime

data['Date'] = pd.to_datetime(data['Date'])

And set it as the index:

data.set_index('Date', inplace=True)

data
Consumption Wind Solar Wind+Solar
Date
2006-01-01 1069.18400 NaN NaN NaN
2006-01-02 1380.52100 NaN NaN NaN
2006-01-03 1442.53300 NaN NaN NaN
2006-01-04 1457.21700 NaN NaN NaN
2006-01-05 1477.13100 NaN NaN NaN
... ... ... ... ...
2017-12-27 1263.94091 394.507 16.530 411.037
2017-12-28 1299.86398 506.424 14.162 520.586
2017-12-29 1295.08753 584.277 29.854 614.131
2017-12-30 1215.44897 721.247 7.467 728.714
2017-12-31 1107.11488 721.176 19.980 741.156

4383 rows × 4 columns

What are the units? See here: it’s \(GWh\).
Rename columns accordingly:

data.rename(axis=1, mapper={col: col+' [GWh]' for col in data.columns}, inplace=True)

data
Consumption [GWh] Wind [GWh] Solar [GWh] Wind+Solar [GWh]
Date
2006-01-01 1069.18400 NaN NaN NaN
2006-01-02 1380.52100 NaN NaN NaN
2006-01-03 1442.53300 NaN NaN NaN
2006-01-04 1457.21700 NaN NaN NaN
2006-01-05 1477.13100 NaN NaN NaN
... ... ... ... ...
2017-12-27 1263.94091 394.507 16.530 411.037
2017-12-28 1299.86398 506.424 14.162 520.586
2017-12-29 1295.08753 584.277 29.854 614.131
2017-12-30 1215.44897 721.247 7.467 728.714
2017-12-31 1107.11488 721.176 19.980 741.156

4383 rows × 4 columns

Looks fine.

Examine data

Now let’s have a look at a plot:

plt.figure(figsize=(16,7))
for col in data.columns:
    plt.plot(data[col], label=col.rstrip('[GWh]'))
plt.title('Consumption and generation energy data',fontsize=16)
plt.xlabel('Date', fontsize=14)
plt.ylabel('Energy [GWh]', fontsize=14)
plt.grid()
plt.legend();
_images/codeChapter_20_0.png

Total energy consumption

How much energy was consumed each year?

data['year'] = data.index.year

# divide by 1000 => TWh
consumptions_years = data.groupby(axis=0, by='year').sum()/1000

# rename columns accordingly
consumptions_years.rename(axis=1, mapper={col: col.replace('GWh', 'TWh') for col in data.columns},
                          inplace=True)

plt.figure(figsize=(16,7))
plt.bar(x=consumptions_years.index, height=consumptions_years[consumptions_years.columns[0]])
plt.title('Consumption energy data',fontsize=16)
plt.xlabel('Date', fontsize=14)
plt.ylabel('Energy [TWh]', fontsize=14)
plt.grid()
_images/codeChapter_26_0.png

About the same each year

See also

How is the consumption for one year?

plt.figure(figsize=(16,7))
plt.plot(data.loc['2017', 'Consumption [GWh]'], '-x')
plt.title('Consumption energy data',fontsize=16)
plt.xlabel('Date', fontsize=14)
plt.ylabel('Energy [GWh]', fontsize=14)
plt.grid()
_images/codeChapter_30_0.png

Looks like 52 weeks a year, with the weekends having lower demand.
Look for the mean consumtion per weekday:

data2017daily = pd.DataFrame(data.loc['2017', 'Consumption [GWh]'])
data2017daily['day'] = data2017daily.index.weekday

# mean for the weekdays
data2017dailyGrouped = data2017daily.groupby(axis=0, by='day').mean()

plt.figure(figsize=(16,7))
plt.bar(x=data2017dailyGrouped.index, height=data2017dailyGrouped['Consumption [GWh]'])
plt.title('Consumption energy data',fontsize=16)
plt.xlabel('Date', fontsize=14)
plt.ylabel('Energy [GWh]', fontsize=14)
plt.grid()
_images/codeChapter_33_0.png

Hint

Numbers 0 to 6 represent the weekdays monday to sunday (see pandas)

Confirmed: weekends have lower consumption.

Total energy generation by renewables

how much energy was produced each year?

bw = 0.35
plt.figure(figsize=(16,7))
plt.bar(x=consumptions_years.index-bw/2, height=consumptions_years[consumptions_years.columns[1]], label='wind',
       width=bw)
plt.bar(x=consumptions_years.index+bw/2, height=consumptions_years[consumptions_years.columns[2]], label='solar',
       width=bw)

plt.title('Generation energy data',fontsize=16)
plt.xlabel('Date', fontsize=14)
plt.ylabel('Energy [TWh]', fontsize=14)
plt.grid()
plt.legend();
_images/codeChapter_38_0.png

Info

There was already generation by renewables before 2010, there is just no data provided in the dataset

Significant rise in generation by windpower since 2014, only slow rise in generation by pv.

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