flasc.analysis.cumulative_production_analysis.compare_cumulative_production

flasc.analysis.cumulative_production_analysis.compare_cumulative_production(df_list, exclude_turbs=[], ws_range=[0.0, 99.0], model_tags=None, print_to_console=True)

Compare energy and wake losses between dataframes.

Calculate the cumulative energy production for a list of Pandas DataFrame timeseries. Then, calculate the error between the first timeseries in the list (typically SCADA or LES) and the remaining timeseries (typically LES and/or FLORIS models).

Parameters:

• df_list (list) -- List of Pandas DataFrame timeseries. The first entry is typically SCADA or LES, and the remaining entries are models to compare.

• exclude_turbs (list, optional) -- List of turbines to exclude from the analysis, e.g., because of poor performance or odd behavior. Defaults to [].

• ws_range (list, optional) -- Wind speed range for filtering the data. When inspecting wake losses, one may want to zoom into the relevant wind

• range (speed) -- performance for different wind speed regions. Defaults to [0.0, 99.0].

• model (typically between 6 and 14 m/s. This also allows you to inspect the) -- performance for different wind speed regions. Defaults to [0.0, 99.0].

• model_tags (list, optional) -- List of string tags for the models. Defaults to None, which will generate tags as "Model 0", "Model 1", etc.

• print_to_console (bool, optional) -- Whether to print the results to the console. Defaults to True.

Raises:

• ValueError -- If input timeseries dataframes in df_list have different timestamps.

• ValueError -- If input timeseries dataframes in df_list have different number of turbines.

• ValueError -- If input timeseries dataframes in df_list already contain a 'pow_ref' column.

Returns:

Dictionary containing the absolute cumulative production

numbers, including errors w.r.t. the first dataframe.

Return type:

table_absolute_cumprod_dict