ClearSky

class ClearSky(quantile_prob=0.95, bw_diurnal=100, bw_annual=10, min_thresh=0, n_jobs=None, backend='loky')[source]

Clear sky transformer for solar data.

This is a transformation which converts a time series from it’s original domain into a percentage domain. The numerator at each time step in the transformation is the input values, the denominator is a weighted quantile of the time series for that particular time step. In the example of solar power transformations, the denominator is an approximation of the clear sky power, and the output of the transformation is the clearness index.

The clear sky power, i.e. the denominator, is calculated on a grid containing each unique combination of time-of-day and day-of-year. The spacing of the grid depends on the frequency of the input data.

The weights are defined using von-mises kernels with bandwidths chosen by the user.

This transformation can be inaccurate at low values, in the solar example during early morning and late evening. Therefore, clear sky values below a threshold can be fixed to zero in the transformed domain. Denominator values of zero are set to zero in the transformed domain by default.

This transformer is based on the work detailed in [1].

Parameters:

quantile_probfloat, default=0.95: The probability level used to calculate the weighted quantile
bw_diurnalfloat, default=100: The bandwidth of the diurnal kernel. This is the kappa value of the von mises kernel for time of day.
bw_annualfloat, default=10: The bandwidth of the annual kernel. This is the kappa value of the von mises kernel for day of year.
min_threshfloat, default=0: The threshold of the clear sky power below which values are set to zero in the transformed domain.
n_jobsint or None, default=None: Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.
backendstr, default=”loky”: Specify the parallelisation backend implementation in joblib, where “loky” is used by default.

Attributes:

is_fitted: Whether fit has been called.

References

[1]

https://doi.org/10.1016/j.solener.2009.05.016

Examples

>>> from sktime.transformations.clear_sky import ClearSky
>>> from sktime.datasets import load_solar
>>> y = load_solar()
>>> transformer = ClearSky()
>>> # takes ~1min
>>> y_trafo = transformer.fit_transform(y)

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.