Power Plants Data¶
In this exercise, we will use the powerplants.csv dataset from the powerplantmatching project. This dataset contains information about various power plants, including their names, countries, fuel types, capacities, and more.
URL: https://raw.githubusercontent.com/PyPSA/powerplantmatching/master/powerplants.csv
Task 1: Load the dataset into a pandas DataFrame.
Notebook Cell
import pandas as pd
url = (
"https://raw.githubusercontent.com/PyPSA/powerplantmatching/master/powerplants.csv"
)
df = pd.read_csv(url, index_col=0)Task 2: Run the function .describe() on the DataFrame.
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df.describe()Task 3: Provide a list of unique fuel types and technologies included in the dataset.
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df.Fueltype.unique()array(['Hydro', 'Hard Coal', 'Natural Gas', 'Nuclear', 'Lignite', 'Wind',
'Solid Biomass', 'Oil', 'Waste', 'Solar', 'Battery',
'Hydrogen Storage', 'Geothermal', 'Heat Storage', 'Other',
'Biogas', 'Mechanical Storage'], dtype=object)Notebook Cell
df.Technology.unique()array(['Pumped Storage', 'Reservoir', 'Run-Of-River', 'Steam Turbine',
'CCGT', 'Onshore', 'OCGT', nan, 'PV', 'Li', 'Csp', 'Offshore',
'Pb', 'NaS', 'NiCd', 'Laes', 'CSP', 'CAES', 'Molten Salt', 'Fe',
'V', 'Flywheel', 'NaNiCl', 'Combustion Engine'], dtype=object)Task 4: Filter the dataset by power plants with the fuel type “Hard Coal”.
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coal = df.loc[df.Fueltype == "Hard Coal"]
coalTask 5: Identify the 5 largest coal power plants. In which countries are they located? When were they built?
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selection = coal.Capacity.nlargest(5).index
selectionIndex([778, 321, 216, 2165, 4205], dtype='int64', name='id')Notebook Cell
coal.loc[selection, ["Name", "Country", "Capacity", "DateIn"]]Task 6: Identify the power plant with the longest name.
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i = df.Name.apply(lambda x: len(x)).argmax()
df.iloc[i]Name Grn Gesundheitszentren Rhein Neckar Ggmbh Pv A...
Fueltype Solar
Technology PV
Set PP
Country Germany
Capacity 0.4455
Efficiency NaN
DateIn 2026.0
DateRetrofit NaN
DateOut NaN
lat 49.549434
lon 8.643048
Duration NaN
Volume_Mm3 NaN
DamHeight_m NaN
StorageCapacity_MWh NaN
EIC {nan}
projectID {'MASTR': {'MASTR-SEE935883906029'}}
Name: 106579, dtype: objectTask 7: Identify the 10 northernmost powerplants. What type of power plants are they?
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index = df.lat.nlargest(10).index
df.loc[index]Task 8: What is the average start year of each fuel type? Sort the fuel types by their average start year in ascending order and round to the nearest integer.
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df.groupby("Fueltype").DateIn.mean().round().sort_values()Fueltype
Hydrogen Storage 1970.0
Hard Coal 1971.0
Hydro 1973.0
Lignite 1978.0
Nuclear 1980.0
Other 2001.0
Oil 2003.0
Geothermal 2004.0
Waste 2005.0
Solid Biomass 2007.0
Biogas 2012.0
Wind 2013.0
Natural Gas 2014.0
Solar 2018.0
Battery 2024.0
Heat Storage NaN
Mechanical Storage NaN
Name: DateIn, dtype: float64Wind and Solar Capacity Factors¶
In this exercise, we will work with a time series dataset containing hourly wind and solar capacity factors for Ireland, taken from model.energy.
Task 1: Use pd.read_csv to load the dataset from the following URL into a pandas DataFrame. Ensure that the time stamps are treated as pd.DatetimeIndex.
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url = "https://model.energy/data/time-series-2b42655fa0b49b73fb15871dba2f7000.csv"
df = pd.read_csv(url, index_col=0, parse_dates=True)Task 2: Calculate the mean capacity factor for wind and solar over the entire time period.
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df.mean()onwind 0.366898
solar 0.099274
dtype: float64Task 3: Calculate the correlation between wind and solar capacity factors.
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df.corr()Task 4: Plot the wind and solar capacity factors for the month of May.
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df.loc["05-2011"].plot(ylabel="capacity factor")<Axes: ylabel='capacity factor'>
Task 5: Plot the weekly average capacity factors for wind and solar over the entire time period.
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df.resample("W").mean().plot()<Axes: >
Task 6: Go to model.energy and retrieve the time series for another region of your choice. Recreate the analysis above and compare the results.