rdtools.degradation.degradation_year_on_year

rdtools.degradation.degradation_year_on_year(energy_normalized, recenter=True, exceedance_prob=95, confidence_level=68.2)

Estimate the trend of a timeseries using the year-on-year decomposition approach and calculate a Monte Carlo-derived confidence interval of slope.

Parameters

  • energy_normalized (pandas.Series) -- Daily or lower frequency time series of normalized system ouput.

  • recenter (bool, default True) -- Specify whether data is internally recentered to normalized yield of 1 based on first year median. If False, Rd_pct is calculated assuming energy_normalized is passed already normalized to the year 0 system capacity.

  • exceedance_prob (float, default 95) -- 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.

Returns

  • Rd_pct (float) -- Estimated degradation relative to the year 0 median system capacity [%/year]

  • confidence_interval (numpy.array) -- confidence interval (size specified by confidence_level) of degradation rate estimate

  • calc_info (dict) --

    • YoY_values - pandas series of right-labeled year on year slopes

    • renormalizing_factor - float of value used to recenter data

    • exceedance_level - the degradation rate that was outperformed with probability of exceedance_prob

    • usage_of_points - number of times each point in energy_normalized is used to calculate a degradation slope. 0: point is never used. 1: point is either used as a start or endpoint. 2: point is used as both start and endpoint for an Rd calculation.