Energy and Climate

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

#load the data production = pd.read_csv("time_series_60min_singleindex_filtered.csv", usecols=(lambda s: s.startswith('utc') | s.startswith('DE')), parse_dates=[0], index_col=0)

#display the first few columns from the dataset production.head(5)

Run the app to see the outputs

Press the run button in the top right corner

#The dataset tracks from 2014 till 2020, but we will concentrate on the period between 2016-2017 production = production.loc[production.index.year == 2016, :]

production.head(5)

Run the app to see the outputs

Press the run button in the top right corner

Run the app to see this chart

Press the run button in the top right corner

production.shape

Run the app to see the outputs

Press the run button in the top right corner

#retrieving information about the 2016-2017 energy dataset. production.info()

Run the app to see the outputs

Press the run button in the top right corner

#To have a view about the statistical summary of the dataset production.describe()

Run the app to see the outputs

Press the run button in the top right corner

# create plot to show explore actual wind generation in Germany for the time period plt.plot(production.index, production['DE_wind_generation_actual']) plt.title('Actual wind generation in Germany in MW') plt.xlim(pd.Timestamp('2016-01-01'), pd.Timestamp('2017-01-01')) plt.ylim(0, 40000) # save plot plt.savefig("figures/wind.png", dpi=200)

Run the app to see the outputs

Press the run button in the top right corner

# create plot plt.plot(production.index, production['DE_solar_generation_actual'], c = 'orange') plt.title('Actual solar generation in Germany in MW') plt.xlim(pd.Timestamp('2016-01-01'), pd.Timestamp('2017-01-01')) plt.ylim(0, 35000) # save plot plt.savefig("figures/solar.png", dpi=200)

Run the app to see the outputs

Press the run button in the top right corner

#Lets store the actual energy production columns in a new dataframe production_wind_solar = production[['DE_wind_generation_actual', 'DE_solar_generation_actual']]

url = "https://storage.googleapis.com/tutorial-datasets/weather_data_GER_2016.csv"

weather = pd.read_csv(url, parse_dates=[0], index_col=0)

# Displays the first few rows of the weather dataset weather.head()

Run the app to see the outputs

Press the run button in the top right corner

# previewing the summarized information about the data weather.info()

Run the app to see the outputs

Press the run button in the top right corner

# To obtain statistical overview of the data weather.describe()

Run the app to see the outputs

Press the run button in the top right corner

#to check for the shape of the weather dataset weather.shape

Run the app to see the outputs

Press the run button in the top right corner

weather.loc[weather.index == '2016-01-01 00:00:00', :]

Run the app to see the outputs

Press the run button in the top right corner

# Now lets group weather for different locations by day and store it in a new dataframe weather_by_day = weather.groupby(weather.index).mean()

weather_by_day.shape

Run the app to see the outputs

Press the run button in the top right corner

weather_by_day.head(24)

Run the app to see the outputs

Press the run button in the top right corner

# plot to visualize frequent weather information for wind velocity at 10m above displace height plt.plot(weather_by_day.index, weather_by_day['v2'], c='OrangeRed') plt.title('Wind velocity at 10m above the displacement height (m/s)') plt.xlim(pd.Timestamp('2016-01-01'), pd.Timestamp('2017-01-01')) plt.ylim(0, 15) # save plot plt.savefig("figures/velocity_10m.png", dpi=200)

Run the app to see the outputs

Press the run button in the top right corner

# plot to visualize frequent weather information for ground horizontal radiation (W/m²) plt.plot(weather_by_day.index, weather_by_day['SWGDN'], c='OrangeRed') plt.title('Ground horizontal radiation (W/m²)') plt.xlim(pd.Timestamp('2016-01-01'), pd.Timestamp('2017-01-01')) plt.ylim(0, 1000) # save plot plt.savefig("figures/radiation.png", dpi=200)

Run the app to see the outputs

Press the run button in the top right corner

# create plot plt.plot(weather_by_day.index, weather_by_day['T'], c='OrangeRed') plt.title('Temperature (K)') plt.xlim(pd.Timestamp('2016-01-01'), pd.Timestamp('2017-01-01')) plt.ylim(263, 303) # save plot plt.savefig("figures/temperature_K.png", dpi=200)

Run the app to see the outputs

Press the run button in the top right corner

# create plot plt.plot(weather_by_day.index, weather_by_day['rho']) plt.title('Air density at the surface (kg/m³)') plt.xlim(pd.Timestamp('2016-01-01'), pd.Timestamp('2017-01-01'))

Run the app to see the outputs

Press the run button in the top right corner

# create plot plt.plot(weather_by_day.index, weather_by_day['p']) plt.title('Air pressure at the surface (Pa)') plt.xlim(pd.Timestamp('2016-01-01'), pd.Timestamp('2017-01-01'))

Run the app to see the outputs

Press the run button in the top right corner

# merge production_wind_solar and weather_by_day DataFrames combined = pd.merge(production_wind_solar, weather_by_day,how='left', left_index=True, right_index=True)

combined.head(10)

Run the app to see the outputs

Press the run button in the top right corner

sns.pairplot(combined, x_vars=['v1', 'v2', 'v_50m', 'z0'], y_vars=['DE_wind_generation_actual'])

Run the app to see the outputs

Press the run button in the top right corner

sns.pairplot(combined, x_vars=['SWTDN', 'SWGDN', 'T', 'rho', 'p'], y_vars=['DE_wind_generation_actual'])

Run the app to see the outputs

Press the run button in the top right corner

sns.pairplot(combined, x_vars=['SWTDN', 'SWGDN', 'T', 'rho', 'p'], y_vars=['DE_solar_generation_actual'])

Run the app to see the outputs

Press the run button in the top right corner

sns.pairplot(combined, x_vars=['v1', 'v2', 'v_50m', 'z0'], y_vars=['DE_solar_generation_actual'])

Run the app to see the outputs

Press the run button in the top right corner

sns.jointplot(x='v1', y='DE_wind_generation_actual', data=combined, kind='reg') sns.jointplot(x='v2', y='DE_wind_generation_actual', data=combined, kind='reg') sns.jointplot(x='v_50m', y='DE_wind_generation_actual',data=combined, kind='reg')

Run the app to see the outputs

Press the run button in the top right corner

sns.jointplot(x='SWTDN', y='DE_solar_generation_actual',data=combined, kind='reg') sns.jointplot(x='SWGDN', y='DE_solar_generation_actual',data=combined, kind='reg')

Run the app to see the outputs

Press the run button in the top right corner

# import necessary modules from sklearn.linear_model import LinearRegression from sklearn.model_selection import cross_val_score # instantiate LinearRegression lr = LinearRegression()

X_wind = combined[['v1', 'v2', 'v_50m', 'z0']] #dependent feartures from the weather data y_wind = combined['DE_wind_generation_actual'] #target data, to be predicted

X_wind = np.nan_to_num(X_wind) y_wind = np.nan_to_num(y_wind)

scores_wind = cross_val_score(lr, X_wind, y_wind, cv=5) print(scores_wind, "\naverage =", np.mean(scores_wind))

Run the app to see the outputs

Press the run button in the top right corner

X_solar = combined[['SWTDN', 'SWGDN', 'T']] # dependent data y_solar = combined['DE_solar_generation_actual'] #target data

lr_scores_solar = cross_val_score(lr, X_sgd_solar, y_sgd_solar, cv=5) print(lr_scores_solar, "\naverage =", np.mean(lr_scores_solar))

Run the app to see the outputs

Press the run button in the top right corner