This tutorial shows how to use the pvlib.tmy
module to read data from TMY2 and TMY3 files.
This tutorial has been tested against the following package versions:
Authors:
# built in python modules
import datetime
import os
import inspect
# python add-ons
import numpy as np
import pandas as pd
# plotting libraries
%matplotlib inline
import matplotlib.pyplot as plt
try:
import seaborn as sns
except ImportError:
pass
import pvlib
pvlib comes packaged with a TMY2 and a TMY3 data file.
# Find the absolute file path to your pvlib installation
pvlib_abspath = os.path.dirname(os.path.abspath(inspect.getfile(pvlib)))
Import the TMY data using the functions in the pvlib.iotools
module.
tmy3_data, tmy3_metadata = pvlib.iotools.read_tmy3(os.path.join(pvlib_abspath, 'data', '703165TY.csv'))
tmy2_data, tmy2_metadata = pvlib.iotools.read_tmy2(os.path.join(pvlib_abspath, 'data', '12839.tm2'))
Print the TMY3 metadata and the first 5 lines of the data.
print(tmy3_metadata)
tmy3_data.head(5)
{'USAF': 703165, 'Name': '"SAND POINT"', 'State': 'AK', 'TZ': -9.0, 'latitude': 55.317, 'longitude': -160.517, 'altitude': 7.0}
Date (MM/DD/YYYY) | Time (HH:MM) | ETR | ETRN | GHI | GHISource | GHIUncertainty | DNI | DNISource | DNIUncertainty | ... | AOD | AODSource | AODUncertainty | Alb | AlbSource | AlbUncertainty | Lprecipdepth | Lprecipquantity | LprecipSource | LprecipUncertainty | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1997-01-01 01:00:00-09:00 | 01/01/1997 | 01:00 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | ... | 0.051 | F | 8 | 0.24 | F | 8 | -9900 | -9900 | ? | 0 |
1997-01-01 02:00:00-09:00 | 01/01/1997 | 02:00 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | ... | 0.051 | F | 8 | 0.24 | F | 8 | -9900 | -9900 | ? | 0 |
1997-01-01 03:00:00-09:00 | 01/01/1997 | 03:00 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | ... | 0.051 | F | 8 | 0.24 | F | 8 | -9900 | -9900 | ? | 0 |
1997-01-01 04:00:00-09:00 | 01/01/1997 | 04:00 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | ... | 0.051 | F | 8 | 0.24 | F | 8 | -9900 | -9900 | ? | 0 |
1997-01-01 05:00:00-09:00 | 01/01/1997 | 05:00 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | ... | 0.051 | F | 8 | 0.24 | F | 8 | -9900 | -9900 | ? | 0 |
5 rows × 68 columns
tmy3_data['GHI'].plot();
The TMY readers have an optional argument to coerce the year to a single value.
tmy3_data, tmy3_metadata = pvlib.iotools.read_tmy3(os.path.join(pvlib_abspath, 'data', '703165TY.csv'), coerce_year=1987)
tmy3_data['GHI'].plot();
Here's the TMY2 data.
print(tmy2_metadata)
print(tmy2_data.head())
{'WBAN': '12839', 'City': 'MIAMI', 'State': 'FL', 'TZ': -5, 'latitude': 25.8, 'longitude': -80.26666666666667, 'altitude': 2.0} year month day hour ETR ETRN GHI GHISource \ 1962-01-01 00:00:00-05:00 62.0 1.0 1.0 1.0 0.0 0.0 0.0 ? 1962-01-01 01:00:00-05:00 62.0 1.0 1.0 2.0 0.0 0.0 0.0 ? 1962-01-01 02:00:00-05:00 62.0 1.0 1.0 3.0 0.0 0.0 0.0 ? 1962-01-01 03:00:00-05:00 62.0 1.0 1.0 4.0 0.0 0.0 0.0 ? 1962-01-01 04:00:00-05:00 62.0 1.0 1.0 5.0 0.0 0.0 0.0 ? GHIUncertainty DNI ... PwatUncertainty AOD \ 1962-01-01 00:00:00-05:00 0.0 0.0 ... 8.0 62.0 1962-01-01 01:00:00-05:00 0.0 0.0 ... 8.0 62.0 1962-01-01 02:00:00-05:00 0.0 0.0 ... 8.0 62.0 1962-01-01 03:00:00-05:00 0.0 0.0 ... 8.0 62.0 1962-01-01 04:00:00-05:00 0.0 0.0 ... 8.0 62.0 AODSource AODUncertainty SnowDepth \ 1962-01-01 00:00:00-05:00 F 8.0 0.0 1962-01-01 01:00:00-05:00 F 8.0 0.0 1962-01-01 02:00:00-05:00 F 8.0 0.0 1962-01-01 03:00:00-05:00 F 8.0 0.0 1962-01-01 04:00:00-05:00 F 8.0 0.0 SnowDepthSource SnowDepthUncertainty LastSnowfall \ 1962-01-01 00:00:00-05:00 A 7.0 88.0 1962-01-01 01:00:00-05:00 A 7.0 88.0 1962-01-01 02:00:00-05:00 A 7.0 88.0 1962-01-01 03:00:00-05:00 A 7.0 88.0 1962-01-01 04:00:00-05:00 A 7.0 88.0 LastSnowfallSource LastSnowfallUncertaint 1962-01-01 00:00:00-05:00 E 7.0 1962-01-01 01:00:00-05:00 E 7.0 1962-01-01 02:00:00-05:00 E 7.0 1962-01-01 03:00:00-05:00 E 7.0 1962-01-01 04:00:00-05:00 E 7.0 [5 rows x 70 columns]