Meteo for Energy - PV - GET Real time series

API service to retrieve hourly energy generation data

The energy generation (in kWh) is required to be uploaded to make accurate forecasts (see example link). These data is stored on Meteo for Energy databases and also available for the user in case third party services require this information.

This example will show:

• How to make a request of the energy generation of a Solar PV power plant.
• Extract the information in a DataFrame format.
• Plot the results

# First of all, import required packages
%matplotlib inline

import datetime
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

url_base = 'https://api.meteoforenergy.com/Meteo_PV/v1'
headers = {}
headers['X-Auth-Token-Site'] = '6e1dad336510539b32143b5eff0f8733'
headers['X-Auth-Token-User'] = '4cd6cf8de97bf10859302d5441c65143'

Now, we can start preparing our request of energy generation for a site.

• We will request all data available for January 2020
• We will use Python format coding to define Date parameters in ISO-8601 format
• We will receive:
  • the time_unix (seconds since 00:00:00 UTC on 1 January 1970)
  • the datetime_iso8601 with UTC time zone in ISO-8601 format
  • the datetime_local as the datetime on site configuration defined local time
  • the value of generated energy in kWh

# URL to use for the API request
url = url_base + '/real/ts/'

# Time period that we will request
dateIni = datetime.datetime(2020,1,1)
dateFin = datetime.datetime(2020,1,31)

# Define required GET parameters for the request
dict_Meteo = {}
dict_Meteo['date_ini'] = dateIni.strftime('%Y-%m-%d')      # Required: date_ini requires ISO-8601 format
dict_Meteo['date_fin'] = dateFin.strftime('%Y-%m-%d')      # Required: date_fin requires ISO-8601 format
dict_Meteo['dph'] = 1                                      # Optional: Data Per Hour: 1=hourly (default), 4=15min, 6=10min

# Request to the API the data defined on the parameters
r = requests.get(url, params=dict_Meteo, headers=headers)

if( r.status_code == 200 ):
    df = pd.DataFrame(r.json()['data'])                             # or r.json().get('data')
    print(df.head())
    print(df.dtypes)

    time_unix          datetime_iso8601    datetime_local  value
0  1577833200  2019-12-31T23:00:00.000Z  2020-01-01 00:00    0.0
1  1577836800  2020-01-01T00:00:00.000Z  2020-01-01 01:00    0.0
2  1577840400  2020-01-01T01:00:00.000Z  2020-01-01 02:00    0.0
3  1577844000  2020-01-01T02:00:00.000Z  2020-01-01 03:00    0.0
4  1577847600  2020-01-01T03:00:00.000Z  2020-01-01 04:00    0.0
time_unix             int64
datetime_iso8601     object
datetime_local       object
value               float64
dtype: object

The response is a JSON that is converted to a DataFrame, but all the types are not correctly parsed (datetimes are objects). So we need to define correctly the datetime columns and define it on our desired time zone (List of tz database time zones), as it is localized in UTC time zone.

# Convert type from object to datetime, and then localize
df['datetime_iso8601'] = pd.to_datetime(df['datetime_iso8601'])                # datetime: object -> datetime64[ns, UTC]
df['datetime_iso8601'] = df['datetime_iso8601'].dt.tz_convert('Europe/Madrid') # datetime: datetime64[ns, UTC] -> datetime64[ns, Europe/Madrid]
print(df.head())
print(df.dtypes)

    time_unix          datetime_iso8601    datetime_local  value
0  1577833200 2020-01-01 00:00:00+01:00  2020-01-01 00:00    0.0
1  1577836800 2020-01-01 01:00:00+01:00  2020-01-01 01:00    0.0
2  1577840400 2020-01-01 02:00:00+01:00  2020-01-01 02:00    0.0
3  1577844000 2020-01-01 03:00:00+01:00  2020-01-01 03:00    0.0
4  1577847600 2020-01-01 04:00:00+01:00  2020-01-01 04:00    0.0
time_unix                                   int64
datetime_iso8601    datetime64[ns, Europe/Madrid]
datetime_local                             object
value                                     float64
dtype: object

Similarly, we can also use Unix Epoch Time to make the same transformation

# datetime: Unix epoch time -> datetime64[ns, Europe/Madrid]
df['datetime_local'] = pd.to_datetime(df['time_unix'], unit='s').dt.tz_localize('UTC') \
                                                                .dt.tz_convert('Europe/Madrid')
print(df.head())
print(df.dtypes)

    time_unix          datetime_iso8601            datetime_local  value
0  1577833200 2020-01-01 00:00:00+01:00 2020-01-01 00:00:00+01:00    0.0
1  1577836800 2020-01-01 01:00:00+01:00 2020-01-01 01:00:00+01:00    0.0
2  1577840400 2020-01-01 02:00:00+01:00 2020-01-01 02:00:00+01:00    0.0
3  1577844000 2020-01-01 03:00:00+01:00 2020-01-01 03:00:00+01:00    0.0
4  1577847600 2020-01-01 04:00:00+01:00 2020-01-01 04:00:00+01:00    0.0
time_unix                                   int64
datetime_iso8601    datetime64[ns, Europe/Madrid]
datetime_local      datetime64[ns, Europe/Madrid]
value                                     float64
dtype: object

Note that we haven't use the response datetime_local directly as some timezones have Daylight Saving Time (DST) activated and some datetimes might repeat or not appear. To avoid raising errors, it's better to proceed as mentioned.

Plot time

It's time to plot the information requested for data validation.

# Set `datetime` as index to show on X axis
fig = plt.figure(figsize=(14,6), dpi=80)
df = df.set_index('datetime_local')
#df['value'].plot()

OX = df.index + pd.Timedelta(minutes=30)     # As values are hourly averages, we centre at XX:30

fig = plt.figure(figsize=(14,6), dpi=80)
plt.plot(OX, df['value'], c='#95c11f', marker='o',lw=2, label=u'Generation')
plt.title('Site real energy generation. %s - %s' % (dateIni.strftime('%Y-%m-%d'),
                                                    dateFin.strftime('%Y-%m-%d')),
                                                    fontsize=24, fontweight='bold')
plt.xlabel('datetime', fontsize=16, fontweight='bold')
plt.ylabel('Energy [$kWh$]', fontsize=16, fontweight='bold')
plt.legend(loc='upper right')
plt.grid()
plt.show()

<Figure size 1120x480 with 0 Axes>