Meteo for Energy - PV & WIND - 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/meteocast/v1'

# Authorization
headers = {}
headers['Authorization'] = 'Bearer eyJ...'          # Required: Your site API key (JWT)

/usr/local/lib/python3.6/dist-packages/pandas/compat/_optional.py:124: UserWarning: Pandas requires version '1.2.1' or newer of 'bottleneck' (version '1.2.0' currently installed).
  warnings.warn(msg, UserWarning)

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

• We will request all data available for January 2021
• 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 + '/ts/real/site'

# Time period that we will request
dateIni = datetime.datetime(2021,1,1)
dateFin = datetime.datetime(2021,1,31)

# Define required GET parameters for the request
params = {}
params['site_id'] = '98fkae'                        # Required: Site identification code
params['date_ini'] = dateIni.strftime('%Y-%m-%d')   # Required: date_ini requires ISO-8601 format
params['date_fin'] = dateFin.strftime('%Y-%m-%d')   # Required: date_fin requires ISO-8601 format
params['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=params, headers=headers)

if( r.status_code == 200 ):
    # Meta
    site_name = r.json()['meta']['name']
    # Data
    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  1609455600  2020-12-31T23:00:00.000Z  2021-01-01 00:00    0.0
1  1609459200  2021-01-01T00:00:00.000Z  2021-01-01 01:00    0.0
2  1609462800  2021-01-01T01:00:00.000Z  2021-01-01 02:00    0.0
3  1609466400  2021-01-01T02:00:00.000Z  2021-01-01 03:00    0.0
4  1609470000  2021-01-01T03:00:00.000Z  2021-01-01 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  1609455600 2021-01-01 00:00:00+01:00  2021-01-01 00:00    0.0
1  1609459200 2021-01-01 01:00:00+01:00  2021-01-01 01:00    0.0
2  1609462800 2021-01-01 02:00:00+01:00  2021-01-01 02:00    0.0
3  1609466400 2021-01-01 03:00:00+01:00  2021-01-01 03:00    0.0
4  1609470000 2021-01-01 04:00:00+01:00  2021-01-01 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  1609455600 2021-01-01 00:00:00+01:00 2021-01-01 00:00:00+01:00    0.0
1  1609459200 2021-01-01 01:00:00+01:00 2021-01-01 01:00:00+01:00    0.0
2  1609462800 2021-01-01 02:00:00+01:00 2021-01-01 02:00:00+01:00    0.0
3  1609466400 2021-01-01 03:00:00+01:00 2021-01-01 03:00:00+01:00    0.0
4  1609470000 2021-01-01 04:00:00+01:00 2021-01-01 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 %s real energy generation. %s - %s' % (site_name,
                                                       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>