Tutorial 4: Data

The PV system power demo now has outputs, calculations and formulas defined. Now it just needs some data. Data are values that are read from sources such as an Excel workbook or a CSV file. Data are different from outputs because data are known before a simulation, whereas outputs are calculated during the simulation. To specify data, subclass DataSource and declare each data by name as class attributes equal to an instance of DataParameter containing the Data Attributes as arguments. Here’s an example for our PV system power model:

from carousel.core.data_sources import DataSource, DataParameter
from carousel.core import UREG
from datetime import datetime
import pvlib


class PVPowerData(DataSource):
    """
    Data sources for PV Power demo.
    """
    latitude = DataParameter(units="degrees", uncertainty=1.0)
    longitude = DataParameter(units="degrees", uncertainty=1.0)
    elevation = DataParameter(units="meters", uncertainty=1.0)
    timestamp_start = DataParameter()
    timestamp_count = DataParameter()
    module = DataParameter()  # a dictionary
    inverter = DataParameter()  # a dictionary
    module_database = DataParameter()  # a list
    inverter_database = DataParameter()  # a list
    Tamb = DataParameter(units="degC", uncertainty=1.0)
    Uwind = DataParameter(units="m/s", uncertainty=1.0)
    surface_azimuth = DataParameter(units="degrees", uncertainty=1.0)
    timezone = DataParameter()

    def __prepare_data__(self):
        # set frequencies
        for k in ('HOURLY', 'MONTHLY', 'YEARLY'):
            self.data[k] = k
            self.isconstant[k] = True
        # apply metadata
        for k, v in self.parameters.iteritems():
            # TODO: this should be applied in data reader using _meta_names from
            # data registry which should use a meta class and all parameter
            # files should have same layout even xlrd and numpy readers, etc.
            self.isconstant[k] = True  # set all data "isconstant" True
            # uncertainty is dictionary
            if 'uncertainty' in v:
                self.uncertainty[k] = {k: v['uncertainty'] * UREG.percent}
        # convert initial timestamp to datetime
        self.data['timestamp_start'] = datetime(*self.data['timestamp_start'])
        # get module and inverter databases
        self.data['module_database'] = pvlib.pvsystem.retrieve_sam(
            self.data['module_database'], path=SANDIA_MODULES
        )
        self.data['inverter_database'] = pvlib.pvsystem.retrieve_sam(
            self.data['inverter_database'], path=CEC_INVERTERS
        )
        # get module and inverter
        self.data['module'] = self.data['module_database'][self.data['module']]
        self.data['inverter'] = (
            self.data['inverter_database'][self.data['inverter']]
        )

Data Attributes

The following data attributes can be passed as arguments to each data parameter. If using positional arguments, then their order is given in the table below, but keyword arguments can be passed to DataParameter in any order.

Attribute Description
units units from Pint
uncertainty measure of uncertainty, typically 2-sigma
isconstant doesn’t vary in dynamic simulations
timeseries index of dynamic simulation inputs

Meta Class Options

Options that apply to the entire data class like the data reader or enabling data caching are specified in a nested Meta class.

Meta Class Option Description
data_reader name of DataReader
data_cache_enabled toggle caching of data from file readers as JSON

Uncertainty and Variance

Uncertainty should be given in units of percent from UREG or it will raise UncertaintyPercentUnitsError when registered in the data registry. Variance is calculated from the square of the uncertainty when the data is instantiated. The data registry also checks when it registers new data that variance is the square of the uncertainty or it raises UncertaintyVarianceError.

Preparing Data

The data superclass doesn’t automatically apply some of the attributes, such as isconstant and uncertainty; these attributes must be applied in the data source class __prepare_data__ method, an abstract method that must be concrete in the subclass or it will raise exceptions.NotImplementedError. If there is nothing to prepare, then use pass.

The prepare data method is good place to handle several tasks.

  • pop values from one data source to another
  • apply uncertainty
  • non-dimensionalize parameters
  • set constants
  • convert types such as string to datetime

Data Readers

Every data source has one of the DataReader classes. The default is the JSONReader. The data readers collect data depending on the attributes of the parameters specified in the data source. There are also some newer readers in the contributions folder, such as ArgumentReader and DjangoModelReader.