Occasionally, PV data can include features such as saturation,
failure, or strong seasonality that should be removed prior or during
examination. This package includes feature correction methods addressing
each of these.
Saturation Removal
Inverter saturation can throw off trends in the data as well. If you
know the saturation limit of the inverter for your data, you can run
this function to remove saturated data points.
test_dfc_removed_saturation <- plr_saturation_removal(test_dfc, var_list, sat_limit = 3000, power_thresh = 0.99)
# fraction of data kept
nrow(test_dfc_removed_saturation)/nrow(test_dfc)
#> [1] 0.994224
System Failures and Soiling
If a user knows or suspects that a period of system failure is
present in the data, this package offers solutions for identifying and
removing these data points. The method plr_failure_test filters data for
particularly low correlation between power and irradiance, and then
executes k-means clustering (with k=2) to identify a cluster of points
which may indicate soiling. It has an option to group data by months or
look over all data. The function removes data which is in the smaller
cluster if the cluster indicates much lower power production per
irradiance and accounts for a small (<.25) portion of the data.
# default values inserted for reference
#df_failure <- plr_failure_test(test_dfc, var_list, corr_thresh = 0.95, plot = FALSE, by_month = FALSE)
# fraction of data kept
#nrow(df_failure)/nrow(test_dfc)
The method includes an option to plot slopes of day-by-day linear
models of irradiance and power production against days. In order to
plot, one must specify a file_path and file_name to save the plot under;
it is not returned within the R environment. The generated boxplot is of
those same slopes, visually identifying outliers which may represent
failures or soiling.
Seasonality Decomposition
Following power prediction, seasonality may still be apparent in the
data. This is often the case in the XbX model, the data-driven nature of
which is prone to leaving in seasonality. Decomposition, the statistical
method of removing seasonality from data, can be performed on such power
predicted data.
test_xbx_wbw_decomp <- plr_decomposition(test_xbx_wbw_res, freq = 52, power_var = 'power_var', time_var = 'time_var', plot = FALSE, plot_file = NULL, title = NULL, data_file = NULL)
# generate a pretty table
knitr::kable(test_xbx_wbw_decomp[1:5, ], caption = "XbX Week-by-Week Decomposition: Resulting Data")
XbX Week-by-Week Decomposition: Resulting Data
| 2699.661 |
53.13425 |
2600.062 |
46.465313 |
1 |
subseries 1 |
FALSE |
1 |
64.56543 |
1 |
2600.062 |
| 2686.040 |
93.35386 |
2599.413 |
-6.727034 |
1 |
subseries 2 |
FALSE |
2 |
33.34219 |
2 |
2599.413 |
| 2711.840 |
96.67574 |
2598.748 |
16.415820 |
1 |
subseries 3 |
FALSE |
3 |
33.58805 |
3 |
2598.748 |
| 2709.884 |
117.10601 |
2598.075 |
-5.296855 |
1 |
subseries 4 |
FALSE |
4 |
34.55537 |
4 |
2598.075 |
| 2697.696 |
109.10639 |
2597.400 |
-8.809981 |
1 |
subseries 5 |
FALSE |
5 |
49.33841 |
5 |
2597.400 |
# make plots of the decomposed data
raw_plot <- ggplot2::ggplot(test_xbx_wbw_decomp, aes(age, raw)) +
geom_point() +
geom_smooth( method = "lm") +
theme_bw()
trend_plot <- ggplot2::ggplot(test_xbx_wbw_decomp, aes(age, trend)) +
geom_point() +
geom_smooth( method = "lm") +
theme_bw()
seasonal_plot <- ggplot2::ggplot(test_xbx_wbw_decomp, aes(age, seasonal)) +
geom_point() +
geom_smooth( method = "lm") +
theme_bw()
raw_plot
#> `geom_smooth()` using formula = 'y ~ x'
trend_plot
#> `geom_smooth()` using formula = 'y ~ x'
seasonal_plot
#> `geom_smooth()` using formula = 'y ~ x'
