rdtools.degradation.degradation_year_on_year

rdtools.degradation.degradation_year_on_year(energy_normalized, recenter=True, exceedance_prob=95, confidence_level=68.2, uncertainty_method='simple', block_length=30, multi_yoy=False)

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.

  • uncertainty_method (string, default 'simple') -- Either 'simple', 'circular_block', or None Determines what bootstrapping method to use to construct confidence intervals and exceedance levels. If None (or anything other than the three alternatives), the algorithm does not construct confidence intervals, is considerably faster, and only returns the Rd_pct.

  • block_length (int, default 30) -- If uncertainty_method is 'circular_block', block_length determines the length of the blocks used in the circular block bootstrapping in number of days. Must be shorter than a third of the time series.

  • multi_yoy (bool, default False) -- Whether to return the standard Year-on-Year slopes where each slope is calculated over points separated by 365 days (default) or multi_year-on-year where points can be separated by N * 365 days where N is an integer from 1 to the length of the dataset in years.

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 year on year slopes with integer index. When multi_yoy=True the index is non-monotonic because multiple overlapping annual slopes can share the same right-endpoint position.

    • 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. With multi_yoy=True, values can be larger than 2 because each point participates in multiple slopes.

    • YoY_times - pandas DataFrame with columns dt_right, dt_center, and dt_left giving, for each entry in YoY_values, the timestamps of the right endpoint, the midpoint, and the left endpoint of the slope. This can be used to recover the original timestamp- indexed behavior of YoY_values (for example, calc_info['YoY_values'].set_axis(calc_info['YoY_times']['dt_right'])).