The GreenLightModel class is a Python wrapper for the GreenLight MATLAB model, which allows for running simulations of greenhouse environments with supplemental lighting. The resulting model simulations are outputted as a Python dictionary and saved as a JSON file, enabling direct use within Python programs and the ability to perform various calculations with the obtained results.
There are two ways to install this application:
Method 1 (Recommended):
Clone the repository and install the dependencies using pip.
$ git clone https://github.com/greenpeer/GreenLightModel
$ cd GreenLightModel
$ pip install -r requirements.txtMethod 2:
To use the GreenLightModel class, you will need to have the GreenLight and DyMoMa models and GreenLight_Extensions downloaded on your local machine. Once you have downloaded this repository, you should put it in the same folder as the GreenLight and DyMoMa models.
Here are the steps to get started:
- Download the GreenLight and DyMoMa models and GreenLight_Extensions on your computer.
- Download the repository containing the GreenLightModel class.
- Place the GreenLightModel class repository in the same folder as the GreenLight and DyMoMa models.
After completing these steps, you will be ready to use the GreenLightModel class in your Python programs.
Ensure MATLAB R2016b or a more recent version installed on your local machine.You can download it from the official website.
git clone https://github.com/davkat1/GreenLight.git
git clone https://github.com/davkat1/DyMoMa.gitInstall the required Python packages by running the following command in your terminal or command prompt:
pip install -r requirements.txtTo install the required MATLAB Engine API for Python package, you can use pip:
python -m pip install matlabengineTo use the GreenLightModel class, import it in your Python script:
from gl_model import GreenLightModelCreate an instance of the GreenLightModel class:
# Instantiate the GreenLightModel with custom parameter values (optional)
model = GreenLightModel()
# Run the model
gl = model.run_green_light(
filename="sample", # add file name for saving file
weatherInput="bei", # Choose name of location, see folder inputs/energyPlus/data/
seasonLength=1 / 24 / 6, # season length in 5 minute intervals
firstDay=1, # Beginning of season (days since January 1)
isMature=False, # Start with a mature crop, use false to start with a small crop
lampType="led", # 'led', 'hps', or 'none'
initial_gl=gl,
)You can now use the various methods provided by the GreenLightModel class.
import time
from gl_model import GreenLightModel
if __name__ == "__main__":
start_time = time.time()
# Instantiate the GreenLightModel with custom parameter values (optional)
model = GreenLightModel()
# Set Innitial values
gl = {
"p": {
"tSpNight": 28.5, # temperature set point dark period [°C]
"tSpDay": 29.5, # temperature set point light period [°C]
},
}
total_lampIn = 0
total_boilIn = 0
total_co2inj = 0
# Set a iteration loop for 1-h demo , run 12 times, each time is 5 minutes interval, you can remove it
for i in range(11):
# Run the model
gl = model.run_green_light(
filename="sample", # add file name for saving file
weatherInput="bei", # Choose name of location, see folder inputs/energyPlus/data/
seasonLength=1 / 24 / 6, # season length in 5 minute intervals
firstDay=1, # Beginning of season (days since January 1)
isMature=False, # Start with a mature crop, use false to start with a small crop
lampType="led", # 'led', 'hps', or 'none'
initial_gl=gl,
)
# Energy consumption of the lamps [MJ m^{-2}]
lampIn = model.calculate_energy_consumption(gl, "qLampIn", "qIntLampIn")
total_lampIn += lampIn
# Energy consumption of the boiler, boilIn [MJ m^{-2}]
boilIn = model.calculate_energy_consumption(gl, "hBoilPipe", "hBoilGroPipe")
total_boilIn += boilIn
# CO2 Use,co2inj,kg/m2
co2inj = model.calculate_energy_consumption(gl, "mcExtAir")
total_co2inj += co2inj
# Close the model
model.quit()
print("--" * 40)
print(f"Energy consumption of the lamps in 1 hour: {total_lampIn} MJ m^{-2}")
print(f"Energy consumption of the boiler in 1 hour: {total_boilIn} MJ m^{-2}")
print(f"CO2 Use in 1 hour: {total_co2inj} kg m^{-2}")
print("--" * 40)
end_time = time.time()
print("Time taken: {:.2f} seconds".format(end_time - start_time))The output of the above code is:
Energy consumption of the lamps in 1 hour: 0.21999999999999997 MJ m^-2
Energy consumption of the boiler in 1 hour: 0.9899917224945389 MJ m^-2
CO2 Use in 1 hour: 0.006845308136989725 kg m^-2The GreenLightModel class provides the following methods:
add_paths(): Adds required paths to MATLAB's search path.update_params(param_dict): Update the values of parameters in the given dictionary, overwriting the default values.run_green_light(lamp_type=None, weather=None, filename=None, param_names=None, param_vals=None, is_mature=False): Runs the GreenLight model with the given parameters,returns the GreenLight model with the completed simulation in python dictionary format. Data of this simulation is given in 5-minute intervals.calculate_energy_consumption(gl, *array_keys): Calculate the energy consumption for the given parameters.quit(): Stop the MATLAB engine.
Add additional required paths to MATLAB's search path.
add_paths(*args)*args: A variable-length list of folder names to add to MATLAB's search path.
- None
Update the values of parameters in the given dictionary, overwriting the default values.
updated_param_dict = update_params(param_dict)param_dict(dict): A dictionary that maps parameter names to their values.
updated_param_dict(dict): An updated dictionary with evaluated values for formula parameters and converted values for non-formula parameters.
Runs the Green Light simulation with specified parameters and weather input.
gl = run_green_light(
filename="output_file",
weatherInput="bei",
seasonLength=1 / 24 / 6,
firstDay=1,
isMature=False,
lampType="led",
)filename: (str) The file name for saving the output data. Default is an empty string, which means the output data will not be saved.weatherInput: (str) The input file name for the weather data, without the file extension. Default is "bei".seasonLength: (float) The length of the season in fraction of a year. Default is 1/24/6.firstDay: (int) The first day of the simulation. Default is 1.isMature: (bool) Whether the crop is mature. Default is True.lampType: (str) The type of lamp used in the simulation. Default is "led". Available options are "hps", "led", and "none".
lamp_type: Lamp type to simulate, may be 'hps', 'led', or 'none'. Default value is 'none'.weather: Weather inputs for the model. If this argument is empty, artificial weather data for a 5-day season will be generated. If this argument is a scalar number, artificial data will be generated for as many days as this scalar number. Otherwise, weather needs to be a 9-column matrix in the following format:- weather(:,1): timestamps of the input [datenum] in 5-minute intervals
- weather(:,2): radiation [$W m^{-2}$] outdoor global irradiation
- weather(:,3): temperature [°C] outdoor air temperature
- weather(:,4): humidity [$kg m^{-3}$] outdoor vapor concentration
- weather(:,5): co2 [$kg{CO2} m^{-3}{air}$] outdoor CO2 concentration
- weather(:,6): wind [$m s^{-1}$] outdoor wind speed
- weather(:,7): sky temperature [°C]
- weather(:,8): temperature of external soil layer [°C]
- weather(:,9): daily radiation sum [$MJ m^{-2} day^{-1}$]
- weather(:,10): elevation [m above sea level] (optional, default is 0)
filename: Name of the file where the simulation results will be saved. If empty or blank (''), no file will be saved.param_names: Array of strings with names of parameters to modify beyond their default values. Example: ["lampsOn" "lampsOff"]. NOTE: "dependent parameters" (those defined in setDepParams) should be changed by changing their defining parameters. For example, setting aPipe as 0 should be done by setting lPipe or phiPipeE as 0. See setDepParams for a list of dependent parameters.param_vals: Array of values (corresponding to param_names) with modified parameter values.is_mature: If true, simulation will start with a mature crop. Default is false.
gl: (dict) A Python dictionary containing the GreenLight model output with the completed simulation. Data of this simulation is given in 5-minute intervals.
Calculate the energy consumption for the relevant parameters.
energy_consumption = calculate_energy_consumption(gl, *array_keys)gl: A GreenLight model instance.array_keys: A list of keys representing the arrays to be combined and analyzed.
energy_consumption(float): The energy consumption in MJ m^{-2}, or CO2 consumption in kg m^-2.
Stop the MATLAB engine.
quit()- None
makeArtificialInput: Make an artificial dataset to use as input for a GreenLight instance.co2ppm2dens: Convert CO2 molar concentration [ppm] to density [kg m^{-3}].day_light_sum: Calculate the light sum from the sun [MJ m^{-2} day^{-1}] for each day. These values will be constant for each day, and change at midnight.generate_datenum_list: Generates a list of MATLAB format datenums starting from the specified start_datenum and with the specified time interval and number of days.params_from_string(): Extracts all the parameters (i.e., variables) from the given formula string.formula_result(): Calculate the dependent parameters for the GreenLight model using the given formula string and parameters.default_output_folder(): Returns the default output folder path for the GreenLight model.data_folder(): Returns the data folder path for the GreenLight model.save_to_json(json_data, filename=None): Saves the data to a JSON file.find_comment(var_name): Finds and returns the comment associated with a variable in the source code.help(): Prints the help text (docstring) describing the class and its dictionaries.
Make an artificial dataset to use as input for a GreenLight instance.
weather = makeArtificialInput(length)length: length of desired dataset (days)
weather: a matrix with 9 columns, in the following format:weather[:,0]: timestamps of the input [datenum] in 5-minute intervalsweather[:,1]: radiation [W m^{-2}] outdoor global irradiationweather[:,2]: temperature [°C] outdoor air temperatureweather[:,3]: humidity [kg m^{-3}] outdoor vapor concentrationweather[:,4]: CO2 [kg{CO2} m^{-3}{air}] outdoor CO2 concentrationweather[:,5]: wind [m s^{-1}] outdoor wind speedweather[:,6]: sky temperature [°C]weather[:,7]: temperature of external soil layer [°C]weather[:,8]: daily radiation sum [MJ m^{-2} day^{-1}]
Convert CO2 molar concentration [ppm] to density [kg m^{-3}].
co2Dens = co2ppm2dens(temp, ppm)temp: given temperatures [°C] (numeric vector)ppm: CO2 concentration in air (ppm) (numeric vector)
Inputs should have identical dimensions
co2Dens: CO2 concentration in air [kg m^{-3}] (numeric vector)
Calculate the light sum from the sun [MJ m^{-2} day^{-1}] for each day. These values will be constant for each day, and change at midnight.
lightSum = day_light_sum(time, rad)time: timestamps of radiation data (datenum format). These timestamps must be in regular intervalsrad: corresponding radiation data (W m^{-2})
lightSum: daily radiation sum, with the same timestamps of time (MJ m^{-2} day^{-1})
Generates a list of MATLAB format datenums starting from the specified start_datenum and with the specified time interval and number of days.
datenum_list = generate_datenum_list(start_datenum, num_days, interval_secs)start_datenum(float): The datenum to start generating from.num_days(int): The number of days to generate datenums for.interval_secs(int): The time interval in seconds between each datenum.
datenum_list(list of floats): The list of MATLAB datenums for the specified time interval and number of days.
Extract all the parameters (i.e., variables) from the given formula string.
params = params_from_string(formular_str)formular_str(str): The formula string to extract parameters from.
params(list): A list of matched words, excluding "pi" and "exp".
Calculate the dependent parameters for the GreenLight model using the given formula string and parameters. Dependent parameters are parameters that depend on the setting of another parameters.
result = formula_result(param_dict, formula_str, para_list)param_dict(dict): A dictionary that maps parameter names to their values.formula_str(str): The formula string to calculate.para_list(list): A list of parameter names used in the formula string.
result(float): The evaluated result of the formula.
Return the default output folder path for the GreenLight model.
output_folder = default_output_folder()output_folder(str): The default output folder path for the GreenLight model.
Return the data folder path for the GreenLight model.
data_folder_path = data_folder()data_folder_path(str): The data folder path for the GreenLight model.
Save the data to a JSON file.
save_to_json(json_data, filename=None)json_data: The JSON data to be saved.filename(str, optional): The name of the file to save the data to. Defaults to "data".
- None
Find the comment associated with the given variable name.
comment = find_comment(var_name)var_name(str): The name of the variable to find the comment for.
comment(str): The comment associated with the given variable name.
- None
Get help for the specified function.
help(function_name)function_name(str): The name of the function to get help for.
- None
current_folder: The folder containing the class source file.output_folder: The folder where the output files will be saved.
- David Katzin, Simon van Mourik, Frank Kempkes, and Eldert J. Van Henten. 2020. “GreenLight - An Open Source Model for Greenhouses with Supplemental Lighting: Evaluation of Heat Requirements under LED and HPS Lamps.” Biosystems Engineering 194: 61–81. https://doi.org/10.1016/j.biosystemseng.2020.03.010
- David Katzin. DyMoMa - Dynamic Modelling for MATLAB. Zenodo. http://doi.org/10.5281/zenodo.3697908
This project is licensed under the MIT License. See the LICENSE file for more information.