def entsoe (self):
client = EntsoePandasClient(api_key='5aa63adb-f9f7-47aa-8053-5cf531a3743f')
start = pd.Timestamp(tomorrow, tz='Europe/Berlin')
end = pd.Timestamp(aftertomorrow, tz='Europe/Berlin')
country_code = 'DE'
df = client.query_wind_and_solar_forecast(country_code, start=start, end=end, psr_type=None)
df1 = client.query_generation_forecast(country_code, start=start, end=end )
df = self.kwh(df)
df['hour']=df.index.hour
df['date']=df.index.date
df['weekDay']=df.index.dayofweek
df['month']=df.index.month
dfgroup = df.groupby(['date','hour']).agg({ 'Solar':'sum', 'Wind Offshore':'sum' ,'Wind Onshore':'sum' ,'weekDay':'max', 'month':'max'})
df2 = pd.DataFrame(data=df1)
df2['hour']=df2.index.hour
df2['date']=df2.index.date
df1group = df2.groupby(['date','hour']).sum()
df1group = df1group * 1000
df1group.rename(columns={0:'Generation'}, inplace=True)
result = pd.concat([df1group,dfgroup], axis=1)
idx = result.index
result.index = result.index.set_levels([idx.get_level_values('date').astype(str), idx.get_level_values('hour')])
return result
ts_load = client.query_load(country_code, start=start, end=end)
ts_load.to_csv('data_base/ts_load.csv')
ts_load_forecast = client.query_load_forecast(country_code,
start=start,
end=end)
ts_load_forecast.to_csv('data_base/ts_load_forecast.csv')
ts_generation_forecast = client.query_generation_forecast(country_code,
start=start,
end=end)
ts_generation_forecast.to_csv('data_base/ts_generation_forecast.csv')
# Dataframes
df_wind_and_solar_forecast = client.query_wind_and_solar_forecast(
country_code, start=start, end=end, psr_type=None)
df_wind_and_solar_forecast.to_csv('data_base/df_wind_and_solar_forecast.csv')
df_generation = client.query_generation(country_code,
start=start,
end=end,
psr_type=None)
df_generation.to_csv('data_base/df_generation.csv')
df_installed_generation_capacity = client.query_installed_generation_capacity(
country_code, start=start, end=end, psr_type=None)
df_installed_generation_capacity.to_csv(
'data_base/df_installed_generation_capacity.csv')
df_crossborder_flows = client.query_crossborder_flows('DE',
'DK',
client = EntsoePandasClient(api_key='444fc771-5d0f-499f-9328-90c05c459219')
#start and end date format
start_date_raw = date.today() + timedelta(days=1)
start_date = start_date_raw.strftime("%Y%m%d")
end_date_raw = date.today() + timedelta(days=2)
end_date = end_date_raw.strftime("%Y%m%d")
#quering load and renewable generation forecasts
start = pd.Timestamp(start_date, tz='Europe/Brussels')
end = pd.Timestamp(end_date, tz='Europe/Brussels')
country_code = 'DE'
load_raw = client.query_load_forecast(country_code, start=start, end=end)
renewable = client.query_wind_and_solar_forecast(country_code,
start=start,
end=end)
#formating load_raw to pandas dataframe
load = pd.DataFrame(load_raw)
#Algorithm
renewable['Total'] = renewable['Solar'] + renewable[
'Wind Offshore'] + renewable['Wind Onshore']
renewable['Renewable/Load_ratio'] = renewable['Total'] / load[0] * 100
renewable['Quality_ratio'] = renewable['Renewable/Load_ratio'] / renewable[
'Renewable/Load_ratio'].max() * 100
#Fomrating data: date, rounding values, etc...
renewable['Date'] = renewable.index
renewable.insert(0, 'id', range(0, len(renewable)))
