rdtools.soiling.SRRAnalysis.run

SRRAnalysis.run(reps=1000, day_scale=13, clean_threshold='infer', trim=False, method='half_norm_clean', clean_criterion='shift', precip_threshold=0.01, min_interval_length=7, exceedance_prob=95.0, confidence_level=68.2, recenter=True, max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5)

Run the SRR method from beginning to end. Perform the stochastic rate and recovery soiling loss calculation. Based on the methods presented in Deceglie et al. "Quantifying Soiling Loss Directly From PV Yield" JPV 8(2) p547 2018.

Parameters

  • reps (int, default 1000) -- number of Monte Carlo realizations to calculate

  • day_scale (int, default 13) -- The number of days to use in rolling median for cleaning detection, and the maximum number of days of missing data to tolerate in a valid interval. An odd value is recommended.

  • clean_threshold (float or 'infer', default 'infer') -- The fractional positive shift in rolling median for cleaning detection. Or specify 'infer' to automatically use outliers in the shift as the threshold.

  • trim (bool, default False) -- Whether to trim (remove) the first and last soiling intervals to avoid inclusion of partial intervals

  • method (str, {'half_norm_clean', 'random_clean', 'perfect_clean'} default 'half_norm_clean') --

    How to treat the recovery of each cleaning event

    • 'random_clean' - a random recovery between 0-100%

    • 'perfect_clean' - each cleaning event returns the performance metric to 1

    • 'half_norm_clean' - The starting point of each interval is taken randomly from a half normal distribution with its mode (mu) at 1 and its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to the interval.

  • clean_criterion (str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} default 'shift') --

    The method of partitioning the dataset into soiling intervals

    • 'precip_and_shift' - rolling median shifts must coincide with precipitation to be a valid cleaning event.

    • 'precip_or_shift' - rolling median shifts and precipitation events are each sufficient on their own to be a cleaning event.

    • 'shift', only rolling median shifts are treated as cleaning events.

    • 'precip', only precipitation events are treated as cleaning events.

  • precip_threshold (float, default 0.01) -- The daily precipitation threshold for defining precipitation cleaning events. Units must be consistent with self.precipitation_daily

  • min_interval_length (int, default 7) -- The minimum duration for an interval to be considered valid. Cannot be less than 2 (days).

  • exceedance_prob (float, default 95.0) -- The probability level to use for exceedance value calculation in percent

  • confidence_level (float, default 68.2) -- The size of the confidence interval to return, in percent

  • recenter (bool, default True) -- Specify whether data is centered to normalized yield of 1 based on first year median

  • max_relative_slope_error (float, default 500) -- the maximum relative size of the slope confidence interval for an interval to be considered valid (percentage).

  • max_negative_step (float, default 0.05) -- The maximum magnitude of negative discrete steps allowed in an interval for the interval to be considered valid (units of normalized performance metric).

  • outlier_factor (float, default 1.5) -- The factor used in the Tukey fence definition of outliers for flagging positive shifts in the rolling median used for cleaning detection. A smaller value will cause more and smaller shifts to be classified as cleaning events.

Returns

  • insolation_weighted_soiling_ratio (float) -- P50 insolation-weighted soiling ratio based on stochastic rate and recovery analysis

  • confidence_interval (numpy.array) -- confidence interval (size specified by confidence_level) of insolation-weighted soiling ratio

  • calc_info (dict) --

    • 'renormalizing_factor' - value used to recenter data

    • 'exceedance_level' - the insolation-weighted soiling ratio that was outperformed with probability of exceedance_prob

    • 'stochastic_soiling_profiles' - List of Pandas series corresponding to the Monte Carlo realizations of soiling ratio profiles

    • 'soiling_ratio_perfect_clean' - Pandas series of the soiling ratio during valid soiling intervals assuming perfect cleaning and P50 slopes

    • 'soiling_interval_summary' - Pandas dataframe summarizing the soiling intervals identified. The columns of the dataframe are as follows:

      Column Name

      Description

      'start'

      Start timestamp of the soiling interval

      'end'

      End timestamp of the soiling interval

      'soiling_rate'

      P50 Soiling rate for interval, in day^−1 Negative value indicates soiling is occurring. E.g. a rate of −0.01 indicates 1% soiling loss per day.

      'soiling_rate_low'

      Low edge of confidence interval for soiling rate for interval, in day^−1

      'soiling_rate_high'

      High edge of confidence interval for soiling rate for interval, in day^−1

      'inferred_start_loss'

      Estimated performance metric at the start of the interval

      'inferred_end_loss'

      Estimated performance metric at the end of the interval

      'length'

      Number of days in the interval

      'valid'

      Whether the interval meets the criteria to be treated as a valid soiling interval