graph LR
ModelChain["ModelChain"]
run_model["run_model"]
prepare_inputs["prepare_inputs"]
effective_irradiance_model["effective_irradiance_model"]
temperature_model["temperature_model"]
dc_model["dc_model"]
ac_model["ac_model"]
losses_model["losses_model"]
run_model -- "initiates" --> ModelChain
ModelChain -- "orchestrates" --> prepare_inputs
ModelChain -- "orchestrates" --> effective_irradiance_model
ModelChain -- "orchestrates" --> temperature_model
ModelChain -- "orchestrates" --> dc_model
ModelChain -- "orchestrates" --> ac_model
ModelChain -- "orchestrates" --> losses_model
prepare_inputs -- "provides data to" --> ModelChain
effective_irradiance_model -- "provides data to" --> ModelChain
temperature_model -- "provides data to" --> ModelChain
dc_model -- "provides data to" --> ModelChain
ac_model -- "provides data to" --> ModelChain
losses_model -- "provides data to" --> ModelChain
The pvlib.modelchain subsystem is designed around the ModelChain class, which acts as the central orchestrator for simulating photovoltaic (PV) system performance. The run_model function serves as the primary entry point, typically instantiating and utilizing a ModelChain object to execute the entire simulation pipeline. Within the ModelChain, a series of interconnected methods—prepare_inputs, effective_irradiance_model, temperature_model, dc_model, ac_model, and losses_model—represent distinct stages of the simulation. prepare_inputs handles initial data processing, followed by effective_irradiance_model for irradiance calculations, temperature_model for cell temperature, dc_model for DC power output, ac_model for AC power conversion, and finally losses_model to account for system losses. This sequential execution within the ModelChain forms a clear data flow, where the output of one stage serves as the input for the next, culminating in the final AC power output.
The central orchestrator and facade for the entire PV system simulation. It initializes and holds references to various sub-model functions, manages the sequential execution of simulation steps, and provides a high-level, configurable interface for users to run complete simulations. It embodies the pipeline pattern by chaining together different processing stages.
Related Classes/Methods:
Serves as the main public entry point for initiating a PV system simulation from raw weather data. It typically instantiates and utilizes a ModelChain object to execute the full simulation pipeline.
Related Classes/Methods:
A method of ModelChain that pre-processes and validates raw input data (e.g., weather, solar position) to ensure it's in the correct format and contains all necessary information for subsequent modeling stages. This is a critical data preparation step in any data processing pipeline.
Related Classes/Methods:
A method of ModelChain that calculates the effective irradiance incident on the PV module. This is a core scientific computation, transforming raw irradiance data into a usable input for power conversion models.
Related Classes/Methods:
A method of ModelChain that determines the operating cell temperature of the PV modules, a critical factor influencing module efficiency. This is a key environmental modeling component.
Related Classes/Methods:
A method of ModelChain that calculates the DC power output of the PV array based on module characteristics and environmental conditions. This is a central computational component of the PV system model.
Related Classes/Methods:
A method of ModelChain that calculates the AC power output from the inverter, converting the DC power to usable AC power. This represents a crucial data transformation and final stage in the power output pipeline.
Related Classes/Methods:
A method of ModelChain that accounts for any additional system losses (e.g., soiling, shading, wiring) not covered by other specific models. This is a final data refinement step in the simulation.
Related Classes/Methods: