PVGIS quickstart — hourly solar radiation (live)¶

The pvgis backend fetches solar-radiation / PV time series from the keyless JRC PVGIS 5.3 REST API. No credentials, no SDK. This notebook pulls one year of hourly radiation for a single point (northern Italy) and inspects the returned pandas.DataFrame.

In [1]:

import tempfile

import pandas as pd
import matplotlib.pyplot as plt

from earthlens.earthlens import EarthLens

OUT = tempfile.mkdtemp()  # downloads also write a CSV here

import tempfile import pandas as pd import matplotlib.pyplot as plt from earthlens.earthlens import EarthLens OUT = tempfile.mkdtemp() # downloads also write a CSV here

Download one year of seriescalc¶

variables=["seriescalc"] selects the hourly radiation tool. A single point=(lat, lon) is one keyless GET; the start / end years bound the window. download() returns the hourly table and writes it to path as CSV.

In [2]:

df = EarthLens(
    data_source="pvgis",
    variables=["seriescalc"],
    start="2020-01-01",
    end="2020-12-31",
    point=(45.0, 8.0),
    path=OUT,
).download(progress_bar=False)

print(df.shape)
df.head()

df = EarthLens( data_source="pvgis", variables=["seriescalc"], start="2020-01-01", end="2020-12-31", point=(45.0, 8.0), path=OUT, ).download(progress_bar=False) print(df.shape) df.head()

2026-06-26 20:44:59.745 | INFO     | earthlens.pvgis.backend:download:327 - PVGIS seriescalc: 8784 row(s) -> C:\Users\main\AppData\Local\Temp\tmpcbc8_jz0\pvgis_seriescalc.csv

2026-06-26 20:44:59.745 | INFO     | earthlens.pvgis.backend:download:328 - PVGIS (c) European Union, 2001-2024 — data from the JRC Photovoltaic Geographical Information System (PVGIS), https://re.jrc.ec.europa.eu/pvg_tools/. Free reuse with attribution.

(8784, 9)

Out[2]:

	time	G(i)	H_sun	T2m	WS10m	Int	lat	lon	product
0	2020-01-01 00:10:00	0.0	0.0	1.81	0.90	0.0	45.0	8.0	seriescalc
1	2020-01-01 01:10:00	0.0	0.0	1.26	0.83	0.0	45.0	8.0	seriescalc
2	2020-01-01 02:10:00	0.0	0.0	0.90	0.76	0.0	45.0	8.0	seriescalc
3	2020-01-01 03:10:00	0.0	0.0	0.69	0.69	0.0	45.0	8.0	seriescalc
4	2020-01-01 04:10:00	0.0	0.0	0.46	0.62	0.0	45.0	8.0	seriescalc

Each row is one hour. G(i) is global in-plane irradiance (W/m²), T2m the air temperature (°C), WS10m the wind speed; lat / lon / product tag the sampled point. The time column is a parsed datetime64.

In [3]:

df.dtypes

df.dtypes

Out[3]:

time       datetime64[us]
G(i)              float64
H_sun             float64
T2m               float64
WS10m             float64
Int               float64
lat               float64
lon               float64
product               str
dtype: object

Plot a few days of irradiance¶

A short window shows the diurnal cycle of G(i).

In [4]:

window = df[df["time"] < "2020-06-04"]
window = window[window["time"] >= "2020-06-01"]

fig, ax = plt.subplots(figsize=(9, 3))
ax.plot(window["time"], window["G(i)"])
ax.set_ylabel("G(i)  [W/m²]")
ax.set_title("PVGIS seriescalc — in-plane irradiance, 1–3 June 2020 (45°N, 8°E)")
fig.autofmt_xdate()
plt.show()

window = df[df["time"] < "2020-06-04"] window = window[window["time"] >= "2020-06-01"] fig, ax = plt.subplots(figsize=(9, 3)) ax.plot(window["time"], window["G(i)"]) ax.set_ylabel("G(i) [W/m²]") ax.set_title("PVGIS seriescalc — in-plane irradiance, 1–3 June 2020 (45°N, 8°E)") fig.autofmt_xdate() plt.show()

Takeaway¶

One keyless call returns a tidy hourly DataFrame you can resample or plot directly. PVGIS already returns the resolved hourly series, so the facade rejects aggregate=; use pandas (df.set_index("time").resample("1D").mean()) for a coarser cadence.