rdtools.degradation.degradation_classical_decomposition¶
-
rdtools.degradation.
degradation_classical_decomposition
(energy_normalized, confidence_level=68.2)¶ Estimate the trend of a timeseries using a classical decomposition approach (moving average) and calculate various statistics, including the result of a Mann-Kendall test and a Monte Carlo-derived confidence interval of slope.
- Parameters
energy_normalized (
pandas.Series
) -- Daily or lower frequency time series of normalized system ouput. Must be regular time series.confidence_level (
float
, default68.2
) -- The size of the confidence interval to return, in percent.
- Returns
Rd_pct (
float
) -- Estimated degradation relative to the year 0 system capacity [%/year]Rd_CI (
numpy.array
) -- The calculated confidence interval bounds.calc_info (
dict
) -- A dict that contains slope, intercept, root mean square error of regression ('rmse'), standard error of the slope ('slope_stderr'), intercept ('intercept_stderr'), and least squares RegressionResults object ('ols_results'), pandas series for the annual rolling mean ('series'), and Mann-Kendall test trend ('mk_test_trend')