rdtools.analysis_chains.TrendAnalysis

class rdtools.analysis_chains.TrendAnalysis(pv, poa_global=None, temperature_cell=None, temperature_ambient=None, gamma_pdc=None, aggregation_freq='D', pv_input='power', windspeed=0, power_expected=None, temperature_model=None, power_dc_rated=None, interp_freq=None, max_timedelta=None)

Class for end-to-end degradation and soiling analysis using sensor_analysis() or clearsky_analysis()

Parameters

  • pv (pandas.Series) -- Right-labeled time series PV energy or power. If energy, should not be cumulative, but only for preceding time step.

  • poa_global (pandas.Series) -- Right-labeled time series measured plane of array irradiance in W/m^2

  • temperature_cell (pandas.Series) -- Right-labeled time series of cell temperature in Celsius. In practice, back of module temperature works as a good approximation.

  • temperature_ambient (pandas.Series) -- Right-labeled time Series of ambient temperature in Celsius

  • gamma_pdc (float) -- Fractional PV power temperature coefficient

  • aggregation_freq (str or pandas.tseries.offsets.DateOffset) -- Pandas frequency specification with which to aggregate normalized PV data for analysis. For more information, see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects

  • pv_input (str) -- 'power' or 'energy' to specify type of input used for pv parameter

  • windspeed (numeric) -- Right-labeled Pandas Time Series or single numeric value indicating wind speed in m/s for use in calculating cell temperature from ambient default value of 0 neglects the wind in this calculation

  • power_expected (pandas.Series) -- Right-labeled time series of expected PV power. (Note: Expected energy is not supported.)

  • temperature_model (str or dict) -- Model parameters for pvlib.temperature.sapm_cell(). Used in calculating cell temperature from ambient. If string, must be a valid entry for sapm model in pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS. If dict, must have keys 'a', 'b', 'deltaT'. See pvlib.temperature.sapm_cell() documentation for details.

  • power_dc_rated (float) -- Nameplate DC rating of PV array in Watts. If omitted, pv output will be internally normalized in the normalization step based on it's 95th percentile (see TrendAnalysis._pvwatts_norm() source).

  • interp_freq (str or pandas.tseries.offsets.DateOffset) -- Pandas frequency specification used to interpolate the input PV power or energy. We recommend using the natural frequency of the data, rather than up or down sampling. Analysis requires regular time series. For more information see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects

  • max_timedelta (pandas.Timedelta) -- The maximum gap in the data to be interpolated/integrated across when interpolating or calculating energy from power

(not all attributes documented here)
filter_params

parameters to be passed to rdtools.filtering functions. Keys are the names of the rdtools.filtering functions. Values are dicts of parameters to be passed to those functions. Also has a special key ad_hoc_filter the associated value is a boolean mask joined with the rest of the filters. filter_params defaults to empty dicts for each function in rdtools.filtering, in which case those functions use default parameter values, ad_hoc_filter defaults to None. See examples for more information.

Type

dict

results

Nested dict used to store the results of methods ending with _analysis

Type

dict

__init__(pv, poa_global=None, temperature_cell=None, temperature_ambient=None, gamma_pdc=None, aggregation_freq='D', pv_input='power', windspeed=0, power_expected=None, temperature_model=None, power_dc_rated=None, interp_freq=None, max_timedelta=None)

Methods

__init__(pv[, poa_global, temperature_cell, ...])

clearsky_analysis([analyses, yoy_kwargs, ...])

Perform entire clear-sky-based analysis workflow.

plot_degradation_summary(case, **kwargs)

Return a figure of a scatter plot and a histogram summarizing degradation rate analysis.

plot_pv_vs_irradiance(case[, alpha])

Plot PV energy vs irradiance, useful in diagnosing things like timezone problems or transposition errors.

plot_soiling_interval(case, **kwargs)

Return a figure visualizing the valid soiling intervals used in stochastic rate and recovery soiling analysis.

plot_soiling_monte_carlo(case, **kwargs)

Return a figure visualizing the Monte Carlo of soiling profiles used in stochastic rate and recovery soiling analysis.

plot_soiling_rate_histogram(case, **kwargs)

Return a histogram of soiling rates found in the stochastic rate and recovery soiling analysis

sensor_analysis([analyses, yoy_kwargs, ...])

Perform entire sensor-based analysis workflow.

set_clearsky([pvlib_location, pv_azimuth, ...])

Initialize values for a clearsky analysis which requires configuration of location and orientation details.