Olivier Brunet

Data Geek :)

9 minute read

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Description of the data set

This dataset contains hourly estimates of an area’s energy potential for 1986-2015 as a percentage of a power plant’s maximum output.

The overall scope of EMHIRES is to allow users to assess the impact of meteorological and climate variability on the generation of wind power in Europe and not to mime the actual evolution of wind power production in the latest decades. For this reason, the hourly wind power generation time series are released for meteorological conditions of the years 1986-2015 (30 years) without considering any changes in the wind installed capacity. Thus, the installed capacity considered is fixed as the one installed at the end of 2015. For this reason, data from EMHIRES should not be compared with actual power generation data other than referring to the reference year 2015.

Content

The data is available at both the national level and the NUTS 2 level. The NUTS 2 system divides the EU into 276 statistical units. Please see the manual for the technical details of how these estimates were generated. This product is intended for policy analysis over a wide area and is not the best for estimating the output from a single system. Please don’t use it commercially.

Acknowledgements

This dataset was kindly made available by the European Commission’s STETIS program. You can find the original dataset here.

Goal of this 1st step

In a similar manner to the solar energy (see part 1, part 2 & part 3) this is the first part of two. Here we’re going to study wind generation on a country level in order to make clusters of countries which present the same profile so that each group can be investigate in more details later.

First look at the dataset

There is one column per country, and each line / record is an hourly estimate

import numpy as np
import pandas as pd
from scipy import stats
import seaborn as sns
import matplotlib.pyplot as plt

pd.options.display.max_columns = 300

import warnings
warnings.filterwarnings("ignore")

path = "../../../datasets/_classified/kaggle/"

df_wind_on = pd.read_csv(path + "wind_generation_by_country.csv")
df_wind_on.head(2)
AT BE BG CH CY CZ DE DK EE ES FI FR EL HR HU IE IT LT LU LV NL NO PL PT RO SI SK SE UK
0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dealing with timestamps

We have to add the corresponding time at the hour level for each row in order to use and analyze this dataset

def add_time(_df):
    "Returns a DF with two new cols : the time and hour of the day"
    t = pd.date_range(start='1/1/1986', periods=_df.shape[0], freq = 'H')
    t = pd.DataFrame(t)
    _df = pd.concat([_df, t], axis=1)
    _df.rename(columns={ _df.columns[-1]: "time" }, inplace = True)
    _df['hour'] = _df['time'].dt.hour
    _df['month'] = _df['time'].dt.month
    _df['week'] = _df['time'].dt.week
    return _df

df_wind_on = add_time(df_wind_on)
df_wind_on.tail(2)
AT BE BG CH CY CZ DE DK EE ES FI FR EL HR HU IE IT LT LU LV NL NO PL PT RO SI SK SE UK time hour month week
262966 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2015-12-31 22:00:00 22 12 53
262967 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2015-12-31 23:00:00 23 12 53

Let’s keep the records of one year and tranpose the dataset, because we need to have one line per region.

from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

df_wind_on = df_wind_on.drop(columns=['time', 'hour', 'month', 'week'])

df_wind_transposed = df_wind_on[-24*365:].T
df_wind_transposed.tail(2)
254208 254209 254210 254211 254212 254213 254214 254215 254216 254217 254218 254219 254220 254221 254222 254223 254224 254225 254226 254227 254228 254229 254230 254231 254232 254233 254234 254235 254236 254237 254238 254239 254240 254241 254242 254243 254244 254245 254246 254247 254248 254249 254250 254251 254252 254253 254254 254255 254256 254257 254258 254259 254260 254261 254262 254263 254264 254265 254266 254267 254268 254269 254270 254271 254272 254273 254274 254275 254276 254277 254278 254279 254280 254281 254282 254283 254284 254285 254286 254287 254288 254289 254290 254291 254292 254293 254294 254295 254296 254297 254298 254299 254300 254301 254302 254303 254304 254305 254306 254307 254308 254309 254310 254311 254312 254313 254314 254315 254316 254317 254318 254319 254320 254321 254322 254323 254324 254325 254326 254327 254328 254329 254330 254331 254332 254333 254334 254335 254336 254337 254338 254339 254340 254341 254342 254343 254344 254345 254346 254347 254348 254349 254350 254351 254352 254353 254354 254355 254356 254357 ... 262818 262819 262820 262821 262822 262823 262824 262825 262826 262827 262828 262829 262830 262831 262832 262833 262834 262835 262836 262837 262838 262839 262840 262841 262842 262843 262844 262845 262846 262847 262848 262849 262850 262851 262852 262853 262854 262855 262856 262857 262858 262859 262860 262861 262862 262863 262864 262865 262866 262867 262868 262869 262870 262871 262872 262873 262874 262875 262876 262877 262878 262879 262880 262881 262882 262883 262884 262885 262886 262887 262888 262889 262890 262891 262892 262893 262894 262895 262896 262897 262898 262899 262900 262901 262902 262903 262904 262905 262906 262907 262908 262909 262910 262911 262912 262913 262914 262915 262916 262917 262918 262919 262920 262921 262922 262923 262924 262925 262926 262927 262928 262929 262930 262931 262932 262933 262934 262935 262936 262937 262938 262939 262940 262941 262942 262943 262944 262945 262946 262947 262948 262949 262950 262951 262952 262953 262954 262955 262956 262957 262958 262959 262960 262961 262962 262963 262964 262965 262966 262967
SE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.02559 0.028774 0.024368 0.029511 0.026991 0.025740 0.015349 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.021616 0.035272 0.053339 0.061699 0.066683 0.028761 0.015128 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025668 0.076981 0.123490 0.127871 0.094509 0.037900 0.017013 0.000000 0.00000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.026893 0.120466 0.231455 0.265153 0.193312 0.072419 0.015935 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025531 0.075039 0.123906 0.138702 0.087216 0.037514 0.014901 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.021719 0.025448 0.029375 0.028795 0.025825 0.018200 0.014727 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025873 0.042821 0.072591 0.080633 0.058003 0.029394 0.016086 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025720 0.024646 0.033946 0.045143 0.034991 0.023347 0.015876 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025784 0.029620 0.038149 0.038235 0.030227 0.025546 0.015927 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025471 0.035349 0.054981 0.060864 0.051826 0.034001 0.016073 0.000000 0.00000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025909 0.035216 0.041221 0.039333 0.034922 0.030504 0.016031 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025530 0.039458 0.075218 0.083126 0.078889 0.051887 0.016154 0.00000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0
UK 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.01436 0.018766 0.035738 0.042646 0.048847 0.043734 0.035139 0.017733 0.008758 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.014415 0.079417 0.193538 0.287261 0.327187 0.289763 0.201915 0.067396 0.008839 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.014523 0.036608 0.062609 0.065165 0.078764 0.085781 0.082844 0.040742 0.00941 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.014904 0.046943 0.120540 0.188751 0.212467 0.179504 0.117221 0.048684 0.010172 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.015829 0.040851 0.074304 0.100413 0.112761 0.090133 0.056462 0.023553 0.010498 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.016210 0.043734 0.065709 0.097748 0.149532 0.177274 0.150674 0.070279 0.011097 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.016101 0.031604 0.055157 0.062935 0.056517 0.054014 0.043407 0.023064 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.015938 0.043679 0.096823 0.135553 0.146867 0.127883 0.086434 0.029047 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.015992 0.031767 0.061956 0.093995 0.110041 0.088555 0.050805 0.030407 0.008839 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.015775 0.072835 0.187119 0.275783 0.311086 0.259574 0.138164 0.055918 0.00903 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.015557 0.025239 0.042863 0.049173 0.046725 0.039328 0.033127 0.020779 0.009193 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.015666 0.082681 0.191144 0.242983 0.214589 0.177763 0.109878 0.04814 0.009247 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2 rows × 8760 columns

How to find the optimal K ? :) using the elbow method i’ve already covered / explained in depth here

How many clusters would you choose ?

A common, empirical method, is the elbow method. You plot the mean distance of every point toward its cluster center, as a function of the number of clusters. Sometimes the plot has an arm shape, and the elbow would be the optimal K.

def plot_elbow_scores(df_, cluster_nb):
    km_inertias, km_scores = [], []

    for k in range(2, cluster_nb):
        km = KMeans(n_clusters=k).fit(df_)
        km_inertias.append(km.inertia_)
        km_scores.append(silhouette_score(df_, km.labels_))

    sns.lineplot(range(2, cluster_nb), km_inertias)
    plt.title('elbow graph / inertia depending on k')
    plt.show()

    sns.lineplot(range(2, cluster_nb), km_scores)
    plt.title('scores depending on k')
    plt.show()


plot_elbow_scores(df_wind_transposed, 20)

png

png

The best nb of k clusters seems to be 8 or 10 even if there isn’t any real elbow on the 1st plot…
So let’s re-train the model with the k number of clusters of 6 :

X = df_wind_transposed

km = KMeans(n_clusters=6).fit(X)
X['label'] = km.labels_
print("Cluster nb / Nb of countries in the cluster", X.label.value_counts())

Cluster nb / Nb of countries in the cluster 3    8
2    8
0    6
5    3
4    2
1    2
Name: label, dtype: int64

Conclusion

At the end, we are able to list the countries in the E.U, countries which share the same profiles when it comes to produces wind energy. To be honest, this was not a difficult task : the data are already cleaned and there is no need to use complicated machine learning here. But knowing the countries with the same characteristics will be usefull for the second step when we will analyze each profile.

You will find below the list of countries in each cluster :

print("Countries grouped by cluster")
for k in range(6):
    print(f'cluster nb : {k}', " ".join(list(X[X.label == k].index)))

Countries grouped by cluster
cluster nb : 0 EE FI LT LV PL SE
cluster nb : 1 ES PT
cluster nb : 2 AT CH CZ HR HU IT SI SK
cluster nb : 3 BE DE DK FR IE LU NL UK
cluster nb : 4 CY NO
cluster nb : 5 BG EL RO

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