Prognostic Methods for Photovoltaic Systems' Underperformance and Degradation: Status, Perspectives, and Challenges.

Day-ahead PV production forecasting can be crucial not only for energy allocation in the day-ahead market, allowing the direct participation in markets of PV power plants and aggregated systems, but also for predictive maintenance. This means that if the correlation between environmental factors and PV degradation rates is known, these can be estimated for any location worldwide, even before PV systems are operational. The number of field data to analyze increases as PV systems become larger, making manual PV monitoring more and more challenging. In [[20]], the authors use long short-term (LSTM) neural networks on a multivariate set of input data: they use publicly available weather reports and measured PV power output. [Extracted from the article]