consmodel 0.2.3

The library aims to provide a simple way to create individual consumer loads, generation.

Welcome to consmodel library 👋

!!! Warning: the library is active and the functionalities are being added on weekly basis, some functionalities will also change !!!

The library aims to provide a simple way to create individual consumer loads and generation.

The library is a centralised modelling tool that implements the following consumption/generation consumptions:

• pure consumption model,
• solar plant model,
• heat pump model,
• electric vehicle modelling,
• possibly other models...

The main idea of the library is to be able to easily create consumption or generation power consumption profiles.

The schema of the library is as follows:

🏠 Homepage

Install

pip3 install consmodel

Usage

PV model

   from consmodel import PV
   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt

   # create a simple PV model
   pv = PV(lat=46.155768,
           lon=14.304951,
           alt=400,
           index=1,
           name="test",
           freq="15min",)
   timeseries = pv.simulate(pv_size=14.,
                            year=2022,
                            model="ineichen",
                            consider_cloud_cover=True)
   # plot the results
   timeseries.plot()
   plt.show()

BS model

   from consmodel import BS
   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt

   # create a simple PV model
   test_consumption = [0.,-3.,-2.,8.,7.,6.,7.,8.,5.,4.,-2.]
   test_consumption_df = pd.DataFrame({"p": test_consumption},
                  index=pd.date_range("2020-01-01 06:00:00",
                                       periods=11,
                                       freq="15min"))
   bs = BS(lat=46.155768,
           lon=14.304951,
           alt=400,
           index=1,
           st_type="10kWh_5kW",
           freq="15min",)
   timeseries = batt.simulate(control_type="installed_power",
                              p_kw=test_consumption_df)
   # plot the results
   timeseries.plot()
   plt.show()

Consumer model

   from consmodel import ConsumerModel
   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt

   cons = ConsumerModel(lat=46.155768,
                        lon=14.304951,
                        alt=400,
                        index=1,
                        name="ConsumerModel_default",
                        tz="Europe/Ljubljana",
                        use_utc=False,
                        freq="15min",)
   timeseries = cons.simulate(has_generic_consumption=False,
                              has_pv=True,
                              has_heatpump=True,
                              has_ev=False,
                              has_battery=True,
                              start=pd.to_datetime("2020-01-01 06:15:00"),
                              end=pd.to_datetime("2020-01-01 06:00:00")+pd.Timedelta("1d"),
                              pv_size=14.,
                              wanted_temp=20.,
                              hp_st_type="Outdoor Air / Water (regulated)",
                              bs_st_type="10kWh_5kW",
                              control_type="production_saving")
   timeseries.plot()
   plt.show()

Author

👤 Blaž Dobravec

• Website: https://github.com/blazdob
• Github: @blazdob
• LinkedIn: @https://www.linkedin.com/in/blaz-dobravec/

Colaborated:

• Matej Oblak: @MatejGitOblak
• Bine Flajnik: @Bine-f

🤝 Contributing

Contributions, issues and feature requests are welcome!

Feel free to check issues page.

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