PV power and the Typical Meteorological Year (live)¶

Beyond raw radiation, PVGIS can model PV system power and synthesise a Typical Meteorological Year (TMY). This notebook switches on PV modelling for seriescalc and pulls a TMY for the same point.

In [1]:

import tempfile

import pandas as pd
import matplotlib.pyplot as plt

from earthlens.earthlens import EarthLens

OUT = tempfile.mkdtemp()

import tempfile import pandas as pd import matplotlib.pyplot as plt from earthlens.earthlens import EarthLens OUT = tempfile.mkdtemp()

PV system power with pvcalculation=1¶

The PV knobs ride through to the PVGIS query: pvcalculation=1 turns on the model, peakpower (kWp), loss (%), angle (tilt °), and aspect (azimuth °, 0 = south) describe the array. The result gains a P column — PV power in W.

In [2]:

pv = EarthLens(
    data_source="pvgis",
    variables=["seriescalc"],
    start="2020-01-01",
    end="2020-12-31",
    point=(45.0, 8.0),
    pvcalculation=1,
    peakpower=1,
    loss=14,
    angle=35,
    aspect=0,
    path=OUT,
).download(progress_bar=False)

print("P" in pv.columns, pv["P"].max(), "W peak")
pv[["time", "P", "G(i)"]].head()

pv = EarthLens( data_source="pvgis", variables=["seriescalc"], start="2020-01-01", end="2020-12-31", point=(45.0, 8.0), pvcalculation=1, peakpower=1, loss=14, angle=35, aspect=0, path=OUT, ).download(progress_bar=False) print("P" in pv.columns, pv["P"].max(), "W peak") pv[["time", "P", "G(i)"]].head()

2026-06-26 20:45:09.432 | INFO     | earthlens.pvgis.backend:download:327 - PVGIS seriescalc: 8784 row(s) -> C:\Users\main\AppData\Local\Temp\tmpsrtrg09x\pvgis_seriescalc.csv

2026-06-26 20:45:09.433 | 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.

True 836.06 W peak

Out[2]:

	time	P	G(i)
0	2020-01-01 00:10:00	0.0	0.0
1	2020-01-01 01:10:00	0.0	0.0
2	2020-01-01 02:10:00	0.0	0.0
3	2020-01-01 03:10:00	0.0	0.0
4	2020-01-01 04:10:00	0.0	0.0

A representative summer day shows the modelled PV power profile.

In [3]:

day = pv[(pv["time"] >= "2020-06-15") & (pv["time"] < "2020-06-16")]

fig, ax = plt.subplots(figsize=(9, 3))
ax.plot(day["time"], day["P"], color="tab:orange")
ax.set_ylabel("P  [W]")
ax.set_title("PVGIS PV power — 1 kWp, 35° tilt, south, 15 June 2020")
fig.autofmt_xdate()
plt.show()

day = pv[(pv["time"] >= "2020-06-15") & (pv["time"] < "2020-06-16")] fig, ax = plt.subplots(figsize=(9, 3)) ax.plot(day["time"], day["P"], color="tab:orange") ax.set_ylabel("P [W]") ax.set_title("PVGIS PV power — 1 kWp, 35° tilt, south, 15 June 2020") fig.autofmt_xdate() plt.show()

A Typical Meteorological Year¶

variables=["tmy"] returns one synthetic 8760-hour year of meteorological variables (temperature, humidity, irradiance components, wind, pressure) — no year window needed.

In [4]:

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

print(tmy.shape)
tmy[["time", "T2m", "RH", "G(h)"]].head()

tmy = EarthLens( data_source="pvgis", variables=["tmy"], start="2020-01-01", end="2020-12-31", point=(45.0, 8.0), path=OUT, ).download(progress_bar=False) print(tmy.shape) tmy[["time", "T2m", "RH", "G(h)"]].head()

2026-06-26 20:45:11.263 | INFO     | earthlens.pvgis.backend:download:327 - PVGIS tmy: 8760 row(s) -> C:\Users\main\AppData\Local\Temp\tmpsrtrg09x\pvgis_tmy.csv

2026-06-26 20:45:11.264 | 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.

(8760, 13)

Out[4]:

	time	T2m	RH	G(h)
0	2018-01-01 00:00:00	2.04	94.38	0.0
1	2018-01-01 01:00:00	1.98	95.45	0.0
2	2018-01-01 02:00:00	1.92	96.51	0.0
3	2018-01-01 03:00:00	1.85	97.57	0.0
4	2018-01-01 04:00:00	1.79	98.64	0.0

Takeaway¶

The same download() shape gives raw radiation, modelled PV power (with the PV knobs), or a TMY — selected entirely by variables= and the keyword knobs. All keyless, all returned as tidy hourly DataFrames.