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example_calls.py
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example_calls.py
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#############################
# THIS SCRIPT IS DEPRECATED #
#############################
import matplotlib.pyplot as plt
import pandas as pd
import entsoe as ent
import parsers
import mykey
import mappings
# # need these imports???
# import os
# import sys
# nb_dir = os.path.split(os.getcwd())[0]
# if nb_dir not in sys.path:
# sys.path.append(nb_dir)
KEY = mykey.get_key() # create mykey.py with get_key method that returns YOUR OWN entso-e api key/token
ent_app = ent.Entsoe(KEY) # my api key/token
##########################
### PLOTTING CONSTANTS ###
##########################
FIG_DIMENSION = (15,7)
##########################
### COUNTRY_MAPPINGS ###
##########################
COUNTRY_MAPPINGS = {
'AL': 'Albania',
'AT': 'Austria',
'BA': 'Bosnia and Herz.',
'BE': 'Belgium',
'BG': 'Bulgaria',
'BY': 'Belarus',
'CH': 'Switzerland',
'CZ': 'Czech Republic',
'CY': 'Cyprus',
'DE': 'Germany',
'DE-AT-LU': '',
'DK': 'Denmark',
'EE': 'Estonia',
'ES': 'Spain',
'FI': 'Finland',
'FR': 'France',
'GB': 'United Kingdom',
'GB-NIR': '',
'GR': 'Greece',
'HR': 'Croatia',
'HU': 'Hungary',
'IE': 'Ireland',
'IT': 'Italy',
'LT': 'Lithuania',
'LU': 'Luxembourg',
'LV': 'Latvia',
# 'MD': 'MD',
'ME': 'Montenegro',
'MK': 'North Macedonia',
'MT': 'Malta',
'NL': 'Netherlands',
'NO': 'Norway',
'PL': 'Poland',
'PT': 'Portugal',
'RO': 'Romania',
'RS': 'Serbia',
'RU': '',
'RU-KGD': '',
'SE': 'Sweden',
'SI': 'Slovenia',
'SK': 'Slovakia',
'TR': 'Turkey',
'UA': 'Ukraine'
}
EU_COUNTRY_CODES = ['AT', 'BE', 'BG', 'HR', 'CY',
'CZ', 'DK', 'EE', 'FI', 'FR',
'DE', 'GR', 'HU', 'IE', 'IT',
'LV', 'LT', 'LU', 'MT', 'NL',
'PL', 'PT', 'RO', 'SK', 'SI',
'ES', 'SE']
# TOP_TEN_CONSUMERS
# df_energy = ent_app.query_price('RO', start_tm, end_tm, as_dataframe=True)
# df_energy = ent_app.query_generation_forecast('RO', start_tm, end_tm, as_dataframe=True)
# df = ent_app.query_generation('RO', start_tm, end_tm, as_dataframe=True)
# df_energy = ent_app.query_installed_generation_capacity('RO', start_tm, end_tm, as_dataframe=True)
''' NOTES ON THE FOUR METHODS ABOVE
method():
What each one procures,
* what needs to be fixed/extended
query_price():
Price document - Transmission day ahead prices
query_generation_forecast():
Wind and solar forecast for a day ahead
query_generation():
Actual generation per production type, realised
query_installed_generation_capacity
Installed Capacity per Production Type (year ahead?)
* probably needs a different kind of graph for one-data point per year per generation type, consider pie-chart?
all methods localize the time zone to the specified country, assuming the original is in UTC (which entso-e now provides by default).
* can comment out tz.convert in all methods to standardize the time to UTC if needed.
'''
def price(country_code, start, end, freq, df_raw=None):
country = COUNTRY_MAPPINGS[country_code]
if df_raw is None:
start_tm = pd.to_datetime(start)
end_tm = pd.to_datetime(end)
df_raw = ent_app.query_price(country_code, start_tm, end_tm, as_dataframe=True)
df = df_raw.resample(freq).mean()
# print(df.head)
fig = plt.rcParams["figure.figsize"] = FIG_DIMENSION
df.plot()
plt.suptitle('Mean Electricity Price in ' + country)
plt.ylabel('Mean Price per MTU Over '+ freq+ ' [EUR/MWh]')
plt.xlabel('Time')
# plt.legend(title="Production Type", loc='upper left', reverse=True)
plt.savefig('Electricity Price in ' + country + ', ' + start + ' - ' + end +', '+freq)
# plt.show()
plt.close()
return df_raw
def generation_forecast(country_code, start, end, freq, df_raw=None):
country = COUNTRY_MAPPINGS[country_code]
if df_raw is None:
start_tm = pd.to_datetime(start)
end_tm = pd.to_datetime(end)
df_raw = ent_app.query_generation_forecast(country_code, start_tm, end_tm, as_dataframe=True)
df = df_raw.resample(freq).sum()
# print(df.head)
fig = plt.rcParams["figure.figsize"] = FIG_DIMENSION
df.plot.area()
plt.suptitle('Electricity Generation Forecast in ' + country)
plt.ylabel('Generation per '+ freq+ ' [MW]')
plt.xlabel('Time')
plt.legend(title="Production Type", loc='upper left', reverse=True)
plt.savefig('Electricity Generation Forecast in ' + country + ', ' + start + ' - ' + end +', '+freq)
# plt.show()
plt.close()
return df_raw
def generation(country_code, start, end, freq, df_raw=None, psr=None):
country = COUNTRY_MAPPINGS[country_code]
if df_raw is None:
start_tm = pd.to_datetime(start)
end_tm = pd.to_datetime(end)
df_raw = ent_app.query_generation(country_code, start_tm, end_tm, as_dataframe=True, psr_type=psr)
df = df_raw.resample(freq).sum()
# print(df.head)
fig = plt.rcParams["figure.figsize"] = FIG_DIMENSION
df.plot.area()
if psr is None:
plt.suptitle('Electricity Generation by Production Type in ' + country)
else:
plt.suptitle('Electricity Generation by ' + ent.PSRTYPE_MAPPINGS[psr] + ' in ' + country)
plt.ylabel('Actual Aggregated per '+ freq+ ' [MW]')
plt.xlabel('Time')
# figure out a way to position the legend outside the plot
# might need to shrink the x-axis and then position legend
# on center left, using bb anchor or smth
plt.legend(title="Production Type", loc='upper left', reverse=True)
if psr is None:
plt.savefig('Electricity Generation by Production Type in ' + country + ', ' + start + ' - ' + end +', '+freq)
else:
plt.savefig('Electricity Generation by '+ ent.PSRTYPE_MAPPINGS[psr] + ' in ' + country + ', ' + start + ' - ' + end +', '+freq)
# plt.show()
plt.close()
return df_raw
def capacity(country_code, start, end, freq, df_raw=None):
return df_raw
def generation_wind_solar(country_code, start, end, freq, df_raw=None):
country = COUNTRY_MAPPINGS[country_code]
if df_raw is None:
start_tm = pd.to_datetime(start)
end_tm = pd.to_datetime(end)
# separately generate wind, solar dataframes
df_raw = ent_app.query_generation(country_code, start_tm, end_tm, as_dataframe=True, psr_type='B16')
df_raw2 = ent_app.query_generation(country_code, start_tm, end_tm, as_dataframe=True, psr_type='B18')
df_raw3 = ent_app.query_generation(country_code, start_tm, end_tm, as_dataframe=True, psr_type='B19')
# combine them into one dataframe
for col_name in df_raw2.columns:
df_raw[col_name] = df_raw2[col_name]
for col_name in df_raw3.columns:
df_raw[col_name] = df_raw3[col_name]
df = df_raw.resample(freq).sum()
# print(df.head)
fig = plt.rcParams["figure.figsize"] = FIG_DIMENSION
df.plot.area()
plt.suptitle('Electricity Generation by Wind and Solar in ' + country)
plt.ylabel('Actual Aggregated per '+ freq+ ' [MW]')
plt.xlabel('Time')
# figure out a way to position the legend outside the plot
# might need to shrink the x-axis and then position legend
# on center left, using bb anchor or smth
plt.legend(title="Production Type", loc='upper left', reverse=True)
plt.savefig('Electricity Generation by Wind and Solar in ' + country + ', ' + start + ' - ' + end +', '+freq)
# plt.show()
plt.close()
return df_raw
# aggregates generation values for 27 EU countries
def EU_generation(start, end, freq, df_raw=None, psr=None):
if df_raw is None:
start_tm = pd.to_datetime(start)
end_tm = pd.to_datetime(end)
df_raw = pd.DataFrame()
for country_code in EU_COUNTRY_CODES:
print(COUNTRY_MAPPINGS[country_code])
df_raw1 = ent_app.query_generation(country_code, start_tm, end_tm, as_dataframe=True, psr_type=psr)
all_columns = df_raw1.columns.union(df_raw)
df = df_raw.resample(freq).sum()
# print(df.head)
fig = plt.rcParams["figure.figsize"] = FIG_DIMENSION
df.plot.area()
if psr is None:
plt.suptitle('Electricity Generation by Production Type in EU')
else:
plt.suptitle('Electricity Generation by ' + ent.PSRTYPE_MAPPINGS[psr] + ' in EU')
plt.ylabel('Actual Aggregated per '+ freq+ ' [MW]')
plt.xlabel('Time')
# figure out a way to position the legend outside the plot
# might need to shrink the x-axis and then position legend
# on center left, using bb anchor or smth
plt.legend(title="Production Type", loc='upper left', reverse=True)
if psr is None:
plt.savefig('Electricity Generation by Production Type in EU, ' + start + ' - ' + end +', '+freq)
else:
plt.savefig('Electricity Generation by '+ ent.PSRTYPE_MAPPINGS[psr] + ' in EU, ' + start + ' - ' + end +', '+freq)
# plt.show()
plt.close()
return df_raw
if __name__ == '__main__':
# time of the day defaults to 00:00
start = '2024-01-01'
end = '2024-05-31'
# MAX PERIOD ALLOWED IS 1 YEAR
# will have to later adjust this by concating dataframes
country_code = 'RO'
# electricity generation BY SOURCE, hourly, daily, weekly, monthly sums
df_generation = generation(country_code, start, end, '1h')
generation(country_code, start, end, '1D', df_generation)
generation(country_code, start, end, '1W', df_generation)
generation(country_code, start, end, '1ME', df_generation)
# electricity generation FORECAST, hourly, daily, weekly, monthly sums
df_forecast = generation_forecast(country_code, start, end, '1h')
generation_forecast(country_code, start, end, '1D', df_forecast)
generation_forecast(country_code, start, end, '1W', df_forecast)
generation_forecast(country_code, start, end, '1ME', df_forecast)
# electricity PRICE; hourly, daily, weekly, monthly means
df_price = price(country_code, start, end, '1h')
price(country_code, start, end, '1D', df_price)
price(country_code, start, end, '1W', df_price)
price(country_code, start, end, '1ME', df_price)
# electricity generation by SOLAR, hourly, daily, weekly, monthly sums
df_generation = generation(country_code, start, end, '1h', psr='B16')
generation(country_code, start, end, '1D', df_generation, psr='B16')
generation(country_code, start, end, '1W', df_generation, psr='B16')
generation(country_code, start, end, '1ME', df_generation, psr='B16')
# electricity generation by WIND ONSHORE, hourly, daily, weekly, monthly sums
df_generation = generation(country_code, start, end, '1h', psr='B19')
generation(country_code, start, end, '1D', df_generation, psr='B19')
generation(country_code, start, end, '1W', df_generation, psr='B19')
generation(country_code, start, end, '1ME', df_generation, psr='B19')
# electricity generation by SOLAR+WIND:
df_solarwind = generation_wind_solar(country_code, start, end, '1h')
df_solarwind = generation_wind_solar(country_code, start, end, '1D', df_solarwind)
df_solarwind = generation_wind_solar(country_code, start, end, '1W', df_solarwind)
df_solarwind = generation_wind_solar(country_code, start, end, '1ME', df_solarwind)
# EU electricity generation