machine learning algorithms

The successful deployment of solar energy systems necessitates accurate forecasting of electricity production by photovoltaic power stations (PPS) to ensure the stable operation of power supply networks. This requirement stems from the need to maintain a real-time balance between electricity generation and consumption, which is achieved through the implementation of complex hierarchical control systems governing available energy sources.

One of the applied geodetic tasks in geodynamics is the detection of anomalous deviations in GNSS time series, which may indicate deformations of the Earth's surface caused by various geophysical phenomena. It is important to note that geodynamic anomalies may be of a local nature, manifesting at a single GNSS station, or of a regional nature, occurring simultaneously across a group of GNSS time series. The objective of this article is to develop a method for detecting geodynamic anomalies in GNSS time series using machine learning algorithms.