def forecast():
# Input
start = datetime.now() - timedelta(hours=5)
end = datetime.now() + timedelta(hours=24)
country_code = 'FR' # France
# Print attempt time
print('Job executed at ' + str(datetime.now()))
print('Fetching forecast from ' + str(start) + ' to ' + str(end))
# Query data
client = EntsoePandasClient(api_key=setting.key)
ts = client.query_load_forecast(country_code, start=start, end=end)
# We could concat historical data to have an accurate curve afterward
#client.query_load(country_code, start=start - timedelta(hours=24),end=end)
# Print sucess time range
print('Retrieved forecast from ' + str(ts.index[0]) + ' to ' +
str(ts.index[-1]))
# Resample data (make sure time ends in 0 or 5)
forecast = ts.resample('5T').interpolate()
# Send data over to server
url = 'http://fastapi/forecast'
data = {
'times': [d.strftime('%Y-%m-%dT%H:%M:%SZ') for d in forecast.index],
'values': list((forecast / 1000 - 42.5).tolist())
}
headers = {"Content-Type": "application/json"}
response = requests.put(url, data=json.dumps(data), headers=headers)
res = response.json()
print('Forecast request result ' + str(res))
# =============================================================================
#"""
key = str(np.genfromtxt('entsoe.txt', dtype='str'))
client = EntsoePandasClient(api_key=key)
# Time Series
# NoMatchingDataError
# ts_day_ahead = client.query_day_ahead_prices(country_code, start=start,end=end)
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,
from entsoe import EntsoePandasClient
import sqlite3
import pandas as pd
from datetime import date, timedelta
import time
end_date = date.today() + timedelta(days=1)
current_date = end_date.strftime("%Y%m%d")
client = EntsoePandasClient(api_key='444fc771-5d0f-499f-9328-90c05c459219')
start = pd.Timestamp('20201109', tz='Europe/Brussels')
end = pd.Timestamp(current_date, tz='Europe/Brussels')
country_code = 'DE'
generation = client.query_load(country_code, start=start, end=end)
generation1.to_csv('outfile1.csv')
print(generation1)
start = pd.Timestamp('20201109', tz='Europe/Brussels')
end_date = date.today() + timedelta(days=2)
current_date = end_date.strftime("%Y%m%d")
end = pd.Timestamp(current_date, tz='Europe/Brussels')
country_code = 'DE'
generation2 = client.query_load_forecast(country_code, start=start, end=end)
generation2.to_csv('outfile2.csv')
print(generation2)
