Example Usage

The code snippets below are practical examples of useful tasks accomplished via PlantPredict’s API. All of the code used in the examples below is available via the source code on Github. Feel free to use and modify the code in your local environment.

Every example assumes that you first import plantpredict and authenticate with Api as shown in Step 3 of API Authentication.

Create Project and Prediction from scratch.

Instantiate a local instance of Project, assigning name, latitude, and longitude.

project = api.project(name="Area 51 Alien Power Plant", latitude=37.23, longitude=-115.80)

Assign location attributes with helper method assign_location_attributes(), and create as the local instance of Project a new entity in the PlantPredict database.


Instantiate a local instance of Prediction, assigning project_id (from the newly created project) and name.

prediction = api.prediction(project_id=project.id, name="Area 51 - Contracted")

Assign the weather_id corresponding to the weather file you want to use (assuming it already exists in the PlantPredict database).

prediction.weather_id = 13628

Instantiate and retrieve the weather file, and ensure that the two pairs of prediction start/end attributes match those of the weather file.

weather = api.weather(id=prediction.weather_id)
prediction.start_date = weather.start_date
prediction.end_date = weather.end_date
prediction.start = weather.start_date
prediction.end = weather.end_date

Import all of the enumeration files relevant to prediction settings. Set ALL of the following model options on the prediction using the enumerations library in enumerations similar to the code below, but to your preferences.

from plantpredict.enumerations import PredictionStatusEnum, TranspositionModelEnum, SpectralShiftModelEnum, \
    DiffuseDirectDecompositionModelEnum, ModuleTemperatureModelEnum, IncidenceAngleModelTypeEnum, \
    AirMassModelTypeEnum, DirectBeamShadingModelEnum, SoilingModelTypeEnum, DegradationModelEnum, \
    TrackingTypeEnum, BacktrackingTypeEnum, DiffuseShadingModelEnum

prediction.diffuse_direct_decomp_model = DiffuseDirectDecompositionModelEnum.NONE
prediction.transposition_model = TranspositionModelEnum.PEREZ
prediction.mod_temp_model = ModuleTemperatureModelEnum.HEAT_BALANCE
prediction.inc_angle_model = IncidenceAngleModelTypeEnum.TABULAR_IAM
prediction.spectral_shift_model = SpectralShiftModelEnum.TWO_PARAM_PWAT_AND_AM
prediction.air_mass_model = AirMassModelTypeEnum.BIRD_HULSTROM
prediction.direct_beam_shading_model = DirectBeamShadingModelEnum.LINEAR
prediction.diffuse_shading_model = DiffuseShadingModelEnum.SCHAAR_PANCHULA
prediction.soiling_model = SoilingModelTypeEnum.CONSTANT_MONTHLY
prediction.monthly_factors = [
    {"month": 1, "month_name": "Jan", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 2, "month_name": "Feb", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 3, "month_name": "Mar", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 4, "month_name": "Apr", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 5, "month_name": "May", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 6, "month_name": "Jun", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 7, "month_name": "Jul", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 8, "month_name": "Aug", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 9, "month_name": "Sep", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 10, "month_name": "Oct", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 11, "month_name": "Nov", "albedo": 0.2, "soiling_loss": 2.0},
    {"month": 12, "month_name": "Dec", "albedo": 0.2, "soiling_loss": 2.0},
prediction.diffuse_direct_decomp_model_executed = True
prediction.use_meteo_dni = False
prediction.use_meteo_poai = False
prediction.degradation_model = DegradationModelEnum.LINEAR_DC
prediction.linear_degradation_rate = 0.5
prediction.first_year_degradation = False
prediction.year_repeater = 3

Create the prediction in the PlantPredict database.


Change the prediction’s status to DRAFT-SHARED to make it accessible to other members of your team (or to another relevant status).

prediction.change_status(new_status=PredictionStatusEnum.DRAFT_SHARED, note="Changed for tutorial.")

Instantiate a local instance of PowerPlant, assigning its project_id and prediction_id.

powerplant = api.powerplant(project_id=project.id, prediction_id=prediction.id)

Add a fixed tilt block, array, inverter, and dc field using add_block(), add_array(), add_inverter() and add_dc_field(), respectively. In this example, the minimum required fields are selected, and the rest are defaulted. Refer to each method’s documentation for information on what other power plant attributes can be configured. Additionally, refer to the PlantPredict User Guide for documentation on power plant hierarchy.

fixed_tilt_block_name = powerplant.add_block()
fixed_tilt_array_name = powerplant.add_array(
fixed_tilt_inverter_name = powerplant.add_inverter(

Assuming there is one DC field on the inverter, the number of strings can be calculated from a DC AC ratio. If there were two identical DC fields on a single inverter, you would use half of the number of strings. For irregular configurations, perform a custom calculation for number of strings in parallel and field dc power.

inverter = powerplant.blocks[0]["arrays"][0]["inverters"][0]
field_dc_power = powerplant.calculate_field_dc_power(dc_ac_ratio=1.20, inverter_setpoint=inverter["setpoint_kw"])
number_of_series_strings_wired_in_parallel = powerplant.calculate_number_of_series_strings_wired_in_parallel(
fixed_tilt_dc_field_name = powerplant.add_dc_field(

You can continue to add new blocks, or even add arrays to blocks, inverters to arrays, etc. The code below is an example of adding a block with a DC field that uses single-axis tracking.

tracker_block_name = powerplant.add_block()
tracker_array_name = powerplant.add_array(
tracker_inverter_name = powerplant.add_inverter(

Assuming the tracker array uses the same inverter set point, module and DC AC ratio, the number of strings in parallel and field DC power calculated previously can be used.

tracker_dc_field_name = powerplant.add_dc_field(

Create the local instance of PowerPlant as a new entity in the PlantPredict database. Since the id’s of the project and prediction created previously were assigned to the PowerPlant, it will automatically attach to the prediction in PlantPredict.


The prediction can now be run.


Download nodal data.

First, set up a dictionary containing the nodal data export options. Set the values to True according to which nodes in the PowerPlant hierarchy you are interested in exporting nodal data. For each block in block_export_options, specify the block number (using the field name). You can add export options for multiple blocks, but in this example we just do one.

export_options = {
    'export_system': True,
    'block_export_options': [{
        "name": 1,
        "export_block": False,
        "export_arrays": True,
        "export_inverters": False,
        "export_dc_fields": True

Instantiate a new prediction using the Prediction class, specifying its id and project_id (visible in the URL of that prediction in a web browser … /projects/{project_id}/prediction/{id}/).

project_id = 13161   # CHANGE TO YOUR PROJECT ID
prediction_id = 147813   # CHANGE TO YOUR PREDICTION ID
prediction = api.prediction(id=prediction_id, project_id=project_id)

Run the prediction.


Retrieve the nodal data of Array 1 (in Block 1) and DC Field 1 (in Block 1 –> Array 1 –> Inverter A). Note that the lowest node (power plant hierarchy-wise) in the input dictionary specifies the nodal data returned.

nodal_data_array = prediction.get_nodal_data(params={
    'block_number': 1,
    'array_number': 1,

nodal_data_dc_field = prediction.get_nodal_data(params = {
    'block_number': 1,
    'array_number': 1,
    'inverter_name': 'A',
    'dc_field_number': 1

For system-level nodal data, call the method with no inputs.

nodal_data_system = prediction.get_nodal_data()

The nodal data returned will be returned as JSON serializable data, as detailed in the documentation for get_nodal_data().

Clone a prediction.

Instantiate the prediction you wish to clone using the Prediction class, specifying its id and project_id (visible in the URL of that prediction in a web browser … /projects/{project_id}/prediction/{id}/).

project_id = 13161   # CHANGE TO YOUR PROJECT ID
prediction_id = 147813   # CHANGE TO YOUR PREDICTION ID
prediction_to_clone = api.prediction(id=prediction_id, project_id=project_id)

Clone the prediction, passing in a name for the new prediction. This will create a new prediction within the same project that is an exact copy (other than the name) of the original prediction.

new_prediction_id = prediction_to_clone.clone(new_prediction_name='Cloned Prediction')

If you wish to change something about the new prediction, instantiate a new Prediction with the returned prediction ID, change an attribute, and call the update() method.

new_prediction = api.prediction(id=new_prediction_id, project_id=project_id)
from plantpredict.enumerations import TranspositionModelEnum    # import at the top of the file
new_prediction.transposition_model = TranspositionModelEnum.HAY

Change the module in a power plant.

Instantiate the powerplant of the prediction of interest using the PowerPlant class, specifying the project_id and prediction_id (visible in the URL of that prediction in a web browser … /projects/{project_id}/prediction/{id}/).

project_id = 13161   # CHANGE TO YOUR PROJECT ID
prediction_id = 147813   # CHANGE TO YOUR PREDICTION ID
powerplant = api.powerplant(prediction_id=prediction_id, project_id=project_id)

Retrieve all of its attributes.


Specify the id of the module you want to replace the power plant’s current module with (visible in the URL of that module in a web browser … /module/{id}/). Retrieve the module.

new_module_id = 3047
new_module = api.module(id=new_module_id)

In order to change the module in Block 1 –> Array 1 –> Inverter A –> DC Field 1, replace the previous module’s data structure, replace the module id, and update the power plant with the the update() method.

powerplant.blocks[0]['arrays'][0]['inverters'][0]['dc_fields'][0]['module'] = new_module.__dict__
powerplant.blocks[0]['arrays'][0]['inverters'][0]['dc_fields'][0]['module_id'] = new_module_id

Change a prediction’s weather file.

Instantiate the prediction of interest using the Prediction class, specifying its id and project_id (visible in the URL of that prediction in a web browser … /projects/{project_id}/prediction/{id}/). Do the same for the project of interest using the Project class.

project_id = 13161   # CHANGE TO YOUR PROJECT ID
prediction_id = 147813   # CHANGE TO YOUR PREDICTION ID
prediction = api.prediction(id=prediction_id, project_id=project_id)
project = api.project(id=project_id)

Retrieve the project and prediction’s attributes.


In this particular case, let’s say you are looking for the most recent Meteonorm weather file within a 5-mile radius of the project site. Search for all weather files within a 5 mile radius of the project’s latitude/longitude coordinates.

w = api.weather()
weathers = w.search(project.latitude, project.longitude, search_radius=5)

Filter the results by only Meteonorm weather files.

from plantpredict.enumerations import WeatherDataProviderEnum  # should import at the top of your file
weathers_meteo = [weather for weather in weathers if int(weather['data_provider']) == WeatherDataProviderEnum.METEONORM]

If there is a weather file that meets the criteria, used the most recently created weather file’s id. If no weather file meets the criteria, download a new Meteonorm (or whatever type you are working with) weather file and use that id.

from plantpredict.enumerations import WeatherSourceTypeAPIEnum
if weathers_meteo:
    created_dates = [w['created_date'] for w in weathers_meteo]
    idx = [w['created_date'] for w in weathers_meteo].index(created_dates[-1])
    weather_id = weathers_meteo[idx]['id']
    weather = api.weather()
    response = weather.download(project.latitude, project.longitude, provider=WeatherSourceTypeAPIEnum.METEONORM)
    weather_id = weather.id

Instantiate weather using the weather id and retrieve all of its attributes.

weather = api.weather(id=weather_id)

Ensure that the prediction start/end attributes match those of the weather file.

prediction.start_date = weather.start_date
prediction.end_date = weather.end_date
prediction.start = weather.start_date
prediction.end = weather.end_date

Change the weather_id of the prediction and update the prediction.

prediction.weather_id = weather_id

Upload raw weather data.

Whether you are starting with an Excel file, CSV file, SQL query, or other data format, the first step is to get your data into a JSON-like format. That format is represented in Python as a list of dictionaries, where each dictionary represents a timestamp of weather data. Depending on the initial data format, you can utilize any of Python’s open-source data tools such as the native csv library or pandas. This tutorial skips that step and loads pre-processed data from this JSON file.

import json
with open('weather_details.json', 'rb') as json_file:
    weather_details = json.load(json_file)

Using the known latitude and longitude of the weather data location, call get_location_info() query crucial location info necessary to populate the weather file’s metadata.

latitude = 35.0
longitude = -119.0
geo = api.geo(latitude=latitude, longitude=longitude)
location_info = geo.get_location_info()

Initialize the Weather entity and populate with the minimum fields required by create(). Note that the weather details time series data loaded in the first step is assigned to weather_details at this point.

from plantpredict.enumerations import WeatherDataProviderEnum
weather = api.weather()
weather.name = "Python SDK Test Weather"
weather.latitude = 35.0
weather.longitude = -119.0
weather.country = location_info['country']
weather.country_code = location_info['country_code']
weather.data_provider = WeatherDataProviderEnum.METEONORM
weather.weather_details = weather_details

Assign additional metadata fields.

weather.elevation = round(geo.get_elevation()["elevation"], 2)
weather.locality = location_info['locality']
weather.region = location_info['region']
weather.state_province = location_info['state_province']
weather.state_province_code = location_info['state_province_code']
weather.time_zone = geo.get_time_zone()['time_zone']
weather.status = LibraryStatusEnum.DRAFT_PRIVATE
weather.data_type = WeatherDataTypeEnum.MEASURED
weather.p_level = WeatherPLevelEnum.P95
weather.time_interval = 60  # minutes
weather.global_horizontal_irradiance_sum = round(
    sum([w['global_horizontal_irradiance'] for w in weather_details])/1000, 2
weather.diffuse_horizontal_irradiance_sum = round(
    sum([w['diffuse_horizontal_irradiance'] for w in weather_details])/1000, 2
weather.direct_normal_irradiance_sum = round(
    sum([w['direct_normal_irradiance'] for w in weather_details])/1000, 2
weather.average_air_temperature = np.round(np.mean([w['temperature'] for w in weather_details]), 2)
weather.average_relative_humidity = np.round(np.mean([w['relative_humidity'] for w in weather_details]), 2)
weather.average_wind_speed = np.round(np.mean([w['windspeed'] for w in weather_details]), 2)
weather.max_air_temperature = np.round(max([w['temperature'] for w in weather_details]), 2)

Create the weather file in PlantPredict with create().


Generate a module file.

Instantiate a Module object.

module = api.module()

Assign basic module parameters from the manufacturer’s datasheet or similar data source.

from plantpredict.enumerations import CellTechnologyTypeEnum, PVModelTypeEnum
module.cell_technology_type = CellTechnologyTypeEnum.CDTE
module.number_of_cells_in_series = 264
module.pv_model = PVModelTypeEnum.ONE_DIODE_RECOMBINATION
module.reference_temperature = 25
module.reference_irradiance = 1000
module.stc_max_power = 430.0
module.stc_short_circuit_current = 2.54
module.stc_open_circuit_voltage = 219.2
module.stc_mpp_current = 2.355
module.stc_mpp_voltage = 182.55
module.stc_power_temp_coef = -0.32
module.stc_short_circuit_current_temp_coef = 0.04
module.stc_open_circuit_voltage_temp_coef = -0.28

Generate single diode parameters using the default algorithm/assumptions.


At this point, the user could simply add the remaining required fields and save the new module. Alternatively, the user can tune the module’s single diode parameters to achieve (close to) a desired effective irradiance response (EIR)/low-light performance. The first step is to define target relative efficiencies at specified irradiance.

module.effective_irradiance_response = [
    {'temperature': 25, 'irradiance': 1000, 'relative_efficiency': 1.0},
    {'temperature': 25, 'irradiance': 800, 'relative_efficiency': 1.0029},
    {'temperature': 25, 'irradiance': 600, 'relative_efficiency': 1.0003},
    {'temperature': 25, 'irradiance': 400, 'relative_efficiency': 0.9872},
    {'temperature': 25, 'irradiance': 200, 'relative_efficiency': 0.944}

How a user chooses to tune the module’s performance is relatively open-ended, but a good place to start is using PlantPredict’s Optimize Series Resistance” algorithm. This will automatically change the series resistance to generate an EIR closer to the target, and re-calculate all single-diode parameters dependent on series resistance.


At any point the user can check the current model-calculated EIR to compare it to the target.

calculated_effective_irradiance_response = module.calculate_effective_irradiance_response()

An IV curve can be generated for the module for reference.

iv_curve_at_stc = module.generate_iv_curve(num_iv_points=250)

The initial series resistance optimization might not achieve an EIR close enough to the target. the user can modify any parameter, re-optimize series resistance or just recalculate dependent parameters, and check EIR repeatedly. This is the open-ended portion of module file generation. Important Note: after modifying parameters, if the user does not re-optimize series resistance, generate_single_diode_parameters_advanced() must be called to re-calculate saturation_current_at_stc, diode_ideality_factor_at_stc, light_generated_current, linear_temperature_dependence_on_gamma, maximum_series_resistance and maximum_recombination_parameter (if applicable).

module.shunt_resistance_at_stc = 8000
module.dark_shunt_resistance = 9000
new_eir = module.calculate_effective_irradiance_response()

Once the user is satisfied with the module parameters and performance, assign other required fields.

from plantpredict.enumerations import ConstructionTypeEnum
module.name = "Test Module"
module.model = "Test Module"
module.manufacturer = "Solar Company"
module.length = 2009
module.width = 1232
module.heat_absorption_coef_alpha_t = 0.9
module.construction_type = ConstructionTypeEnum.GLASS_GLASS

Create a new Module in the PlantPredict database.


Set a prediction’s monthly factors (albedo, soiling loss, spectral loss).

Monthly albedo, soiling loss [%], and spectral loss [%] can all be set for a prediction with the attribute monthly_factors (a py:data:dict). This can be done upon initial creation of a prediction from scratch (see the example for Create Project and Prediction from scratch.), but for the sake of example, we will consider the case of updating an existing prediction.

First instantiate the prediction of interest using the Prediction class, specifying its id and project_id (visible in the URL of that prediction in a web browser … /projects/{project_id}/prediction/{id}/).

project_id = 13161  # CHANGE TO YOUR PROJECT ID
prediction_id = 147813  # CHANGE TO YOUR PREDICTION ID
prediction = api.prediction(id=prediction_id, project_id=project_id)

Retrieve the prediction’s attributes.


This example assumes that the user wants to specify all 3 available monthly_factors, and enforce that the prediction use monthly soiling loss and spectral loss averages. (Alternatively, a user can choose to only specify albedo, or albedo and soiling loss, or albedo and spectral shift.)

Set the monthly_factors as such, where albedo is in units [decimal], soiling loss in [%], and spectral loss in [%]. (Note: for soiling loss and spectral loss, a negative number indicates a gain.) The values below should be replaced with those obtained from measurements or otherwise relevant to the project being modeled.

prediction.monthly_factors = [
    {"month": 1, "month_name": "Jan", "albedo": 0.4, "soiling_loss": 0.40, "spectral_shift": 0.958},
    {"month": 2, "month_name": "Feb", "albedo": 0.3, "soiling_loss": 0.24, "spectral_shift": 2.48},
    {"month": 3, "month_name": "Mar", "albedo": 0.2, "soiling_loss": 0.76, "spectral_shift": 3.58},
    {"month": 4, "month_name": "Apr", "albedo": 0.2, "soiling_loss": 0.88, "spectral_shift": 3.48},
    {"month": 5, "month_name": "May", "albedo": 0.2, "soiling_loss": 0.81, "spectral_shift": 2.58},
    {"month": 6, "month_name": "Jun", "albedo": 0.2, "soiling_loss": 1.01, "spectral_shift": 1.94},
    {"month": 7, "month_name": "Jul", "albedo": 0.2, "soiling_loss": 1.21, "spectral_shift": 3.7},
    {"month": 8, "month_name": "Aug", "albedo": 0.2, "soiling_loss": 0.99, "spectral_shift": 4.57},
    {"month": 9, "month_name": "Sep", "albedo": 0.2, "soiling_loss": 1.34, "spectral_shift": 6.39},
    {"month": 10, "month_name": "Oct", "albedo": 0.2, "soiling_loss": 0.54, "spectral_shift": 4.16},
    {"month": 11, "month_name": "Nov", "albedo": 0.3, "soiling_loss": 0.52, "spectral_shift": 0.758},
    {"month": 12, "month_name": "Dec", "albedo": 0.4, "soiling_loss": 0.33, "spectral_shift": 0.886}

In order to enforce that the prediction use monthly average values (rather than soiling time series from a weather file, for instance), the attributes soiling_model and spectral_shift_model must be set with the following code (assuming that both soiling loss and spectral shift loss have been specified in monthly factors).

from plantpredict.enumerations import SoilingModelTypeEnum, SpectralShiftModelEnum
prediction.soiling_model = SoilingModelTypeEnum.CONSTANT_MONTHLY
prediction.spectral_shift_model = SpectralShiftModelEnum.MONTHLY_OVERRIDE

Call the update() method on the instance of Prediction to persist these changes to PlantPredict.


Model System-Level of Power Plant (Transformer, Transmission, etc.)

This tutorial details how to model Total System Capacity, Transformers and Transmission Lines for a power plant/energy prediction. This can be done upon initial creation of a prediction from scratch (see the example for Create Project and Prediction from scratch.), but for the sake of example, we will consider the case of updating an existing power plant.

Instantiate a PowerPlant, specifying its project_id and prediction_id (visible in the URL of that prediction in a web browser … /projects/{project_id}/prediction/{id}).

project_id = 13161   # CHANGE TO YOUR PROJECT ID
prediction_id = 147813   # CHANGE TO YOUR PREDICTION ID
powerplant = api.powerplant(project_id=project_id, prediction_id=prediction_id)

Retrieve the power plant’s attributes.


Set the system availability_loss on the PowerPlant instance in units [%].

powerplant.availability_loss = 1.7

Set the plant output (LGIA) limit in units [MWac].

powerplant.lgia_limitation = 0.8

Add transformers and transmission_lines, specifying the ordinal (1-indexed) such that they are in the desired order (where 1 is closest to the physical output of the plant).

powerplant.add_transformer(rating=0.6, high_side_voltage=600, no_load_loss=1.1, full_load_loss=1.7, ordinal=1)
powerplant.add_transmission_line(length=3, resistance=0.1, number_of_conductors_per_phase=1, ordinal=2)

Call the update() method on the instance of PowerPlant to persist these changes to PlantPredict.