Skip to content

A Python package to calculate degree days (DD or in french DJU - degré jour unifié) from measured outdoor temperatures and to make it possible to quantify drift of energy consumption for heating (or cooling)

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
img
 
 
 
 
 
 

Welcome to pandas_degreedays's documentation!

Latest Version Supported Python versions Wheel format License Development Status Downloads monthly Requirements Status Documentation Status Sourcegraph Gitter Code Health Build Status

pandas_degreedays

Pandas Degree Days (pandas_degreedays) is a Python package to calculate degree days.

Usage

You must provide a Pandas Series with temperature values.

Let's call ts_temp this Serie which looks like:

datetime
2014-03-20 23:00:00    11
2014-03-20 23:30:00    11
2014-03-21 00:00:00    11
2014-03-21 00:30:00    11
2014-03-21 01:00:00    11
2014-03-21 01:30:00    11
...
2014-11-01 20:00:00    12
2014-11-01 20:30:00    12
2014-11-01 21:00:00    12
2014-11-01 21:30:00    12
2014-11-01 22:00:00    12
2014-11-01 22:30:00    12
Name: temp, Length: 10757

You can get a time serie with temperature in sample folder and read it using:

import pandas as pd
filename = 'temperature_sample.xls'
df_temp = pd.read_excel(filename)
df_temp = df_temp.set_index('datetime')
ts_temp = df_temp['temp']

You can also fetch a time serie with temperature from OpenWeatherMap.org. You need to install first openweathermap_requests.

import logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
from pandas_degreedays.provider import TemperatureProvider
api_key = 'YOUR_API_KEY'
ts_temp = TemperatureProvider('OpenWeatherMap', api_key=api_key).get_from_coordinates(0.34189, 46.5798114, '20150101', '20150915')
#ts_temp = TemperatureProvider('OpenWeatherMap', api_key=api_key).get_from_place('Poitiers,FR', '20150101', '20150915')

We can see if some data are missing using:

idx = ts_temp.index
s_idx = pd.Series(idx, index=idx)
diff_idx = s_idx-s_idx.shift(1)
s_sampling_period = diff_idx.value_counts()
sampling_period = s_sampling_period.index[0] # most prevalent sampling period
not_sampling_period = (diff_idx != sampling_period) # True / False

We can interpolate linearly missing data using:

from pandas_degreedays import inter_lin_nan 
ts_temp = inter_lin_nan(ts_temp, '1H') # interpolates linearly NaN

We can calculate degree days using:

from pandas_degreedays import calculate_dd
df_degreedays = calculate_dd(ts_temp, method='pro', typ='heating', Tref=18.0, group='yearly')

method can be:

  • 'pro' (energy professionals) - this is default calculation method
  • 'meteo'

typ (calculation type) can be :

  • 'heating' - this is default calculation type
  • 'cooling'

Tref is reference temperature - default value is 18.0

group can be:

  • 'yearly' - this is default grouping option
  • 'yearly10' - same as 'yearly' but year starts in October (10)
  • 'monthly'
  • 'weekly'
  • None
  • Any lambda function that can be use and that can be applied to a datetime:

Example:

from pandas_degreedays import yearly_month
df_degreedays = calculate_dd(ts_temp, method='pro', typ='heating', Tref=18.0, group=lambda dt: yearly_month(dt, 10))

It outputs a Pandas DataFrame with degree days like:

Tmin  Tmax   Tavg  Tref         DD      DD_cum
2014-03-22 7.0 11.0 9.00 18 9.000000 9.000000
2014-03-23 3.0 12.0 7.50 18 10.500000 19.500000
2014-03-24 0.0 10.0 5.00 18 13.000000 32.500000
2014-03-25 6.0 10.0 8.00 18 10.000000 42.500000
2014-03-26 5.0 12.0 8.50 18 9.500000 52.000000
2014-03-27 2.0 8.0 5.00 18 13.000000 65.000000
... ... ... ... ... ... ...
2014-10-26 5.0 17.0 11.00 18 7.000000 653.547663
2014-10-27 9.0 22.0 15.50 18 3.336923 656.884586
2014-10-28 7.5 20.0 13.75 18 4.544400 661.428986
2014-10-29 8.0 19.0 13.50 18 4.618182 666.047168
2014-10-30 12.0 22.0 17.00 18 1.992000 668.039168
2014-10-31 11.0 24.0 17.50 18 2.143077 670.182245

[224 rows x 6 columns]

You can display plot using:

from pandas_degreedays import plot_temp
plot_temp(ts_temp, df_degreedays)

About Pandas

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It's a very convenient library to work with time series.

Install

From Python package index

$ pip install pandas_degreedays

From source

Get latest version using Git

$ git clone https://github.com/scls19fr/pandas_degreedays.git
$ cd pandas_degreedays
$ python setup.py install

Links

About

A Python package to calculate degree days (DD or in french DJU - degré jour unifié) from measured outdoor temperatures and to make it possible to quantify drift of energy consumption for heating (or cooling)

Topics

Resources

License

Releases

No releases published

Packages

No packages published

Languages