PV data comparison

Overview

These functions automate the creation of PV performance plots, including:

• Capacity factor time-series comparisons (generate_PV_timeseries_plots())

• Scatter plots between measured and simulated data (generate_PV_timeseries_plots())

• Error metrics (mean difference, MAE, RMSE) (generate_PV_timeseries_plots())

• High-resolution plots for clear and cloudy sky conditions (generate_high_res_PV_plots())

All results are automatically saved in:

results/{location_name}/Time series analysis results/

The functions automatically create missing output directories.
If data for the specified year is unavailable, the functions skip plotting.

All plot aesthetics (colors, linestyles, widths) are managed and can be changed in the style_config module.

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Timeseries analysis plots

The function generate_PV_timeseries_plots() generates standard PV time-series comparison plots between measured and simulated datasets:

• Capacity factor time-series plots

• Scatter comparison plots

• Error metrics bar charts (mean difference, MAE, RMSE)

Parameters:

Name	Type	Description
data_sim_meas	pandas.DataFrame	Contains measured ("PV-MEAS") and simulated PV data.
location_name	str	Location name (e.g., “Turin”).
year	str	Year of the analyzed dataset.
output_root	str, optional	Root directory for results (default: “results”).

Example:

from simeasren import generate_PV_timeseries_plots, prepare_pv_data_for_plots
df, _, _ = prepare_pv_data_for_plots("Turin", "2019")
generate_PV_timeseries_plots(df, "Turin", "2019")

Output:

• *_Capacity_Factors.png

• *_scatterplot.png

• *_Errors_Analysis.png

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Plot high resolution PV data

The function generate_high_res_PV_plots() reates high-resolution PV plots for clear-sky and cloudy-sky conditions.

Parameters:

Name	Type	Description
clear_sky_df	pandas.DataFrame	High-frequency PV data for clear-sky days.
cloudy_sky_df	pandas.DataFrame	High-frequency PV data for cloudy-sky days.
location_name	str	Location name (e.g., “Almeria”).
year	str	Year of data.
output_root	str, optional	Root directory for output (default: “results”).

Example:

from simeasren import generate_high_res_PV_plots, prepare_pv_data_for_plots
_, clear_df, cloudy_df = prepare_pv_data_for_plots("Utrecht", "2017")
generate_high_res_PV_plots(clear_df, cloudy_df, "Utrecht", "2017")

Output:

• *_highres_clear_vs_cloudy.png

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Generate all plots

generate_PV_plots()calls both generate_PV_timeseries_plots() and generate_high_res_PV_plots() for a complete analysis in a single step and creates:

• All time-series comparison plots (capacity factor, scatter, error metrics)

• High-resolution plots for clear and cloudy conditions

Parameters:

Name	Type	Description
data_sim_meas	pandas.DataFrame	Measured and simulated PV time-series data.
clear_sky_df	pandas.DataFrame	High-resolution clear-sky PV data.
cloudy_sky_df	pandas.DataFrame	High-resolution cloudy-sky PV data.
location_name	str	Location name (e.g., “Utrecht”).
year	str	Year of analysis.
output_root	str, optional	Output directory (default: “results”).

Example:

from simeasren import generate_PV_plots, prepare_pv_data_for_plots
data_sim_meas, clear_df, cloudy_df = prepare_pv_data_for_plots("Almeria", "2023")
generate_PV_plots(data_sim_meas, clear_df, cloudy_df, "Almeria", "2023")

Generated files:

• Almeria2023_Capacity_Factors.png

• Almeria2023_scatterplot.png

• Almeria2023_Errors_Analysis.png

• Almeria_highres_clear_vs_cloudy.png