Package: ghepy
GHEpyGHEpy(Greenhouse energy-python) is a python tool for evaluation of heating demand in greenhouses when data are not provided elsewhere.Table of contentsDescriptionProblem definitionProblem solutionDependencies and installationExamples and TutorialsAuthors and contributorsLicenseDescriptionGHEpyis a Python-based model implemented for greenhouses according to the energy balance equations. This calculation is intended as a guide for estimation purposes.Problem definitionTo estimate the heating demand of a greenhouseGHEpyneeds parameters like location, dimantions, and inside minimum temperature. The required dimentions for the model are shown below:Problem solutionThe 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 installationGHEpyrequiresnumpy,plotly,CoolProp,folium,vincent. The code is tested for Python 3, while compatibility of Python 2 is not guaranteed anymore. It can be installed usingpipor directly from the source code.Installing via PIPTo install the package just type:>pipinstallGHEpyTo uninstall the package:>pipuninstallGHEpyExamples and TutorialsGHEpyhas 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 fromrenewable ninja platform.First function isenergymodelwhich gives you energy demand of the greenhouse in 8760 hours of a year. To use this function just type:>fromghepyimportmodeltoken="@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@"# Token code that you should get from renewable ninja databaselat=52.225121# latitudelon=36.681990# longitudeT_i=18# minimum inside temperatureh=3# dimensionL=100# dimensiond=100# dimensionheating_demand=model.greenhouse.energymodel(token,lat,lon,T_i,h,L,d,G=2,U=4)The second funciton isvisualizationwhich gives you a figure of temperatures and energy demands during a year. An example of the result of this function can be seen here:>fromghepyimportmodeltoken="@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@"# Token code that you should get from renewable ninja databaselat=52.225121# latitudelon=36.681990# longitudeT_i=18# minimum inside temperatureh=3# dimensionL=100# dimensiond=100# dimensionfig=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 isCDFmapwhich 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.>fromghepyimportmodeltoken="aa643a1899ea2156807425008360759c4853484d"# Token code that you should get from renewable ninja databaselat=52.225121# latitudelon=36.681990# longitudeT_i=18# minimum inside temperatureh=3# dimensionL=100# dimensiond=100# dimensionmap=model.greenhouse.CDFmap(token,lat,lon,T_i,h,L,d,G=2,U=4)mapThe result would be something like this:Authors and contributorsGHEpyis developed and mantained byMehran Ahmadpourunder the supervision of Prof. Ramin Roshandel.Contact us by email for further information or questions aboutGHEpy, or suggest pull requests. Contributions improving either the code or the documentation are welcome!
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