## 1 Introduction  
The development of distributed generation (DG) is aimed at generating electrical energy near the point of its consumption and introducing energy sources with relatively low power and compact dimensions. Typically used are installations running on diesel or gas and renewable energy sources (RES) such as photovoltaic/solar power plants (SPP), wind power plants and mini-hydroelectric power plants. DG is especially important for supplying energy to small settlements in geographically remote areas (Arctic, mountainous, etc.) where, for technical and economic reasons, it is impossible to connect users to the general energy system and guarantee the effective centralised control of network modes [1; 2]. However, when compiling power balances for energy districts with DGs based on RES, the generation forecasting problem arises; this is especially aggravated in the absence of redundancy, when the power station operates in an isolated mode from a large power system [3; 4]. Partially, the problem of discrepancies in power balances can be solved by means of energy storage systems based on batteries, but the capacity of such systems is significantly lower than the possible deviations of actual generation from the forecast. Capacity expansion is limited by economic feasibility due to the high cost of energy storage systems. There are several main groups of time series forecasting methods that are used for renewable energy sources [5]:  
1. Numerical weather prediction (NWP);
2. Statistical methods;
3. Artificial intelligence methods. The first group of models uses a lot of meteorological data and complex models of atmospheric movement [6]. Studies like [7] offer methods for assessing the influence of clouds, ambient temperature and other factors on the generation of photovoltaic power plants. This approach places heavy demands on the volume and quality of meteorological data, thereby often including the need to use satellite imagery or data from an extensive network of multiple weather stations. In addition, it has very high computational complexity. On the contrary, statistical methods are much simpler and, in general, can be applied in the presence of retrospective data only on the predicted value, that is, the level of insolation or SPP generation. The most frequently used autoregressive models are autoregressive (AR), autoregressive integrated moving average (ARIMA), and their modifications [8,9]. However, the problem under consideration can only be solved with high accuracy by taking into account many meteorological factors and complex dependencies that are non-stationary within a day and over longer intervals. Static methods are not effective enough in this case. Due to the complexity and high cost of NWP and the lack of accuracy of statistical models, machine learning models are widely used. These models can analyse multiple input factors. In this case, models based on artificial neural networks [5,10] and ensemble models [3,11,12] can be distinguished. Deep neural network models have been implemented to model and forecast solar irradiance data with the use of meteorological and geographic parameters [13, 14, 15]. Many studies have compared the accuracy obtained by different models. Below are examples of such work. In article [16], hourly solar irradiance was predicted for five weather stations using four years of data, applying models based on support vector regression (SVR), decision trees (DTs), and multi-layer perceptron (MLP). Also, for each of the models, a bagging or gradient boosting algorithm was used. The authors demonstrated an improvement in forecast accuracy when using ensemble methods: the quality of models relative to basic predictors in the context of the root mean squared error increased on average from 5% to 12%, and the fitting speed increased by 7-10%. The best performance was achieved with MLP-boosting/MLP-bagging/DT-boosting (coefficient of determination R\({}^{2}\) from 0.95 to 0.97).