ghepy 0.1.0

(Greenhouse energy-python) is a python tool for evaluation of heating demand in greenhouses when data are not provided elsewhere.

GHEpy

GHEpy (Greenhouse energy-python) is a python tool for evaluation of heating demand in greenhouses when data are not provided elsewhere.

Table of contents

• Description

  • Problem definition
  • Problem solution

• Dependencies and installation

• Examples and Tutorials

• Authors and contributors

• License

Description

GHEpy is a Python-based model implemented for greenhouses according to the energy balance equations. This calculation is intended as a guide for estimation purposes.

Problem definition

To estimate the heating demand of a greenhouse GHEpy needs parameters like location, dimantions, and inside minimum temperature. The required dimentions for the model are shown below:

Problem solution

The energy balance of a greenhouse can be calculated by:

$Q̇_g (t)= Q̇_{con}(t)+ Q̇_l(t)+ Q̇_{trans}(t)+Q̇_{vent}(t)-Q̇_s(t)$

Where:

• $Q ̇_g (t)$ is the required heating energy to maintain greenhouse conditions.
• $Q̇_{con}(t)$ is energy transfer by conduction and convection mechanisms.
• $Q̇_l(t)$ is the energy exchange due to long-wave and short-wave radiations.
• $Q̇_{trans}(t)$ is the energy flow rate caused by crop transpiration.
• $Q̇_{vent}(t)$ is the heat flow rate due to mass transfer for ventilation
• $Q̇_s(t)$ is the solar irradiation energy transfer.

Dependencies and installation

GHEpy requires numpy, plotly, CoolProp, folium, vincent. The code is tested for Python 3, while compatibility of Python 2 is not guaranteed anymore. It can be installed using pip or directly from the source code.

Installing via PIP

To install the package just type:

> pip install GHEpy

To uninstall the package:

> pip uninstall GHEpy

Examples and Tutorials

GHEpy has three main functions that can help you with calculating heating demand of a greenhouse to use any of them you need to have climate data. To acquire data, you just need to take a token from renewable ninja platform.

• First function is energymodel which gives you energy demand of the greenhouse in 8760 hours of a year. To use this function just type:

    >
    from ghepy import model
    token = "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@" # Token code that you should get from renewable ninja database
    lat = 52.225121 # latitude
    lon = 36.681990 # longitude
    T_i = 18 # minimum inside temperature
    h = 3 # dimension
    L = 100 # dimension
    d = 100 # dimension
  
    heating_demand = model.greenhouse.energymodel(token, lat, lon, T_i, h, L, d, G= 2, U = 4)
  

• The second funciton is visualization which gives you a figure of temperatures and energy demands during a year. An example of the result of this function can be seen here:

    >
      from ghepy import model
      token = "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@" # Token code that you should get from renewable ninja database
      lat = 52.225121 # latitude
      lon = 36.681990 # longitude
      T_i = 18 # minimum inside temperature
      h = 3 # dimension
      L = 100 # dimension
      d = 100 # dimension
  
      fig = model.greenhouse.visualization(token, lat, lon, T_i, h, L, d, G= 2, U = 4)
      fig.show()
  

  The result would be something like this:

  

• The last function is CDFmap which shows the location of greenhouse and cumulative distribution of greenhouse heating demand. This can help you realize how long your greenhouse needs heating or cooing during a year.

   >
     from ghepy import model
     token = "aa643a1899ea2156807425008360759c4853484d" # Token code that you should get from renewable ninja database
     lat = 52.225121 # latitude
     lon = 36.681990 # longitude
     T_i = 18 # minimum inside temperature
     h = 3 # dimension
     L = 100 # dimension
     d = 100 # dimension
  
     map = model.greenhouse.CDFmap(token, lat, lon, T_i, h, L, d, G= 2, U = 4)
     map
  

  The result would be something like this:

  

Authors and contributors

GHEpy is developed and mantained by

• Mehran Ahmadpour

under the supervision of Prof. Ramin Roshandel.

Contact us by email for further information or questions about GHEpy, or suggest pull requests. Contributions improving either the code or the documentation are welcome! You can find out more about my projects by visiting my website.

License

See the LICENSE file for license rights and limitations (MIT).