Changes in external conditions, parameters of object functioning, relationships between system elements and system connections with the supersystem lead to a decrease in the accuracy of the artificial intelligence models results, which is called model degradation. Reducing the risk of model degradation is relevant for electric power engineering tasks, the peculiarity of which is multifactor dependencies in complex technical systems and the influence of meteorological parameters. Therefore, automatic updating of models over time is a necessary condition for building user confidence in forecasting systems in power engineering tasks and industry implementations of such systems. There are various methods used to prevent degradation, including an algorithm for detecting data drift, an algorithm for updating models, their retraining, additional training, and fine-tuning. This article presents the results of a study of drift types, their systematization and classification by various features. The solution options that developers need to make when creating intelligent forecasting systems to determine a strategy for updating forecast models are formalized, including update trigger criteria, model selection, hyperparameter optimization, and the choice of an update method and data set formation. An algorithm for forming a strategy for automatic updating of artificial intelligence models is proposed and practical recommendations are given for developers of models in problems of forecasting time series in the power industry, such as forecasting electricity consumption, forecasting the output of solar, wind and hydroelectric power plants.

Keywords: time series forecasting, artificial intelligence, machine learning, trusted AI system, model degradation, data drift, concept drift

The article considers the assessment of the suitability of solar radiation data from ERA5 atmospheric reanalysis for forecasting problems in the northern territories. The experimental site of the Mukhrino station (Khanty-Mansiysk Autonomous Okrug), equipped with an autonomous power supply system, was chosen as the object of analysis. A statistical analysis of the annual array of global horizontal insolation data obtained using the PVGIS platform has been carried out. Seasonal and diurnal features of changes in insolation are considered, distribution profiles are constructed, and emissions are estimated using the interquartile range method. It is established that the data are characterized by high variability and the presence of a large number of zero values due to polar nights and weather conditions. The identified features must be taken into account when building short-term forecasting models. The conclusion is made about the acceptable quality of ERA5 data for use in forecasting energy generation and consumption in heating systems.

Keywords: ERA5, solar radiation, horizontal insolation, the Far North, statistical analysis, forecasting, emissions analysis, renewable energy sources, energy supply to remote areas, time series, intelligent generation management