Signal Processing on PV Time-Series Data: Robust Degradation Analysis without Physical Models

Abstract

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.