Data Description
The dataset consists of current-voltage(\(I-V\)) features obtained by \(I-V\) feature extraction from \(I-V\) curves of photovoltaic (PV) modules.
The features have been extracted using the ddiv package algorithm for
brand A PV modules under damp heat indoor accelerated exposures for up
to 3000 hours. The I-V curves were measured in a step-wise manner after
every 500h of exposure time, and provided by the SunEdison company. The
I-V features include max power (\(P_{mp}\)), short circuit current (\(I_{sc}\)), current at max power (\(I_{mp}\)), fill factor (\(FF\)), series resistance (\(R_s\)), shunt resistance (\(R_{sh}\)), open circuit voltage(\(V_{oc}\)), voltage at max power (\(V_{mp}\)). \(R_{sh}\) is too noisy to contain for
modeling. After checking the correlation between \(I_{sc}\), \(I_{mp}\), \(V_{oc}\), \(V_{mp}\), \(FF\), \(R_s\), we find that \(FF\), \(R_s\), \(V_{mp}\) are highly correlated, and
therefore only choose one of these three variables to be included in the
model. Here we choose \(I_{sc}\), \(I_{mp}\), \(R_s\) and \(V_{oc}\) to be contained in the model and
these four I-V features show no indication of high correlation. The
trend of the I-V features are related with the mechanisms of PV
degradation. The variable ‘\(dy\)’ is
time that has been converted into decimal years so that \(dy\)=1 corresponds to 1 year of exposure
time. We use this dataset to build an <S|M|R> network model with
time (\(dy\)) as the exogenous stressor
variable, four I-V features (\(I_{sc}\), \(I_{mp}\), \(R_s\) and \(V_{oc}\)) as mechanistic endogenous
variables and maximum power (\(P_{mp}\)) as the endogenous response
variable.
Load data and run code to build netSEM
## Load the acrylic data set
data("IVfeature")
## Run netSEMp1 model
ans1 <- netSEMp1(IVfeature)
## Plot the network model for principle 1
plot(ans1, cutoff = c(0.25, 0.5, 0.8))
## Run netSEMp2 model
ans2 <- netSEMp2(IVfeature)
## Plot the network model for principle 2
plot(ans2, cutoff = c(0.25, 0.5, 0.8))
Network diagram for data
The direct <S|R> path from dy to \(P_{mp}\) has an \(adj. R^2\) of 0.757. Considering the path
with one mechanism, the paths contain \(I_{mp}\) or \(R_s\) are likely to be as good as the
direct path. The path from \(dy\) to
\(R_s\) has an \(adj. R^2\) of 0.761 and the path from \(R_s\) to \(P_{mp}\) has an \(adj. R^2\)of 0.912. The path from dy to
\(I_{mp}\) has an \(adj. R^2\) of 0.683 and the path from \(I_{mp}\) to \(P_{mp}\) has an \(adj. R^2\) of 0.829. And we further use the
pathwayRMSE function to calculate the root mean squared error(RMSE) of
the direct path and the two paths with \(I_{mp}\) or \(R_s\). The RMSE for the direct path is
2.8998, for the path contain \(I_{mp}\)
is 3.3007, for the path contain \(R_s\)
is 2.9053. So overall, the predicted accuracy of the direct path and the
path contain \(R_s\) are very similar
and the latter one with \(R_s\) informs
us about the active the degradation mechanism.
