  A novel unsupervised machine learning approach for analyzing time-series data
is applied to the topic of photovoltaic (PV) system degradation rate
estimation, sometimes referred to as energy-yield degradation analysis. This
approach only requires a measured power signal as an input--no irradiance data,
temperature data, or system configuration information. We present results on a
data set that was previously analyzed and presented by NREL using RdTools,
validating the accuracy of the new approach and showing increased robustness to
data anomalies while reducing the data requirements to carry out the analysis.
