Change-Point Detection on Solar Panel Performance Using Thresholded LASSO

Solar energy is a fast growing energy source and has allowed the development of efficient, affordable, and easy-to-install photovoltaic systems over the years. Solar energy stakeholders are, however, concerned with sudden deterioration of photovoltaic systems' performance. Thus, effective change-point detection in solar panel performance analysis is essential for better harnessing solar energy and making photovoltaic systems more efficient. In particular, this study focuses on retrospectively identifying the time points of abrupt changes. Because the power generations from the solar panels are affected by a wide variety of factors, it is very difficult, if not impossible, to find a parametric model to detect abrupt changes in the power generation. We present a nonparametric detection method based on thresholded least absolute shrinkage and selection operator. The proposed method has low computational complexity and is able to accurately detect performance changes while being robust against false detection under noisy signals. The performance of the proposed method in detection of abrupt changes is evaluated and compared with state-of-the-art methods through extensive simulations and a case study using data collected from four solar energy facilities. We demonstrate that the proposed method is superior to benchmark methods. The proposed method will help solar energy stakeholders in several aspects including operations planning, maintenance scheduling, warranty underwriting, and cost–benefit analysis.