## 1 Introduction
On the basis of solar irradiance and meteorological factors data for 2.5 years, study [17] proposed to forecast hourly irradiance with MLP, having previously analysed the dataset with k-means clustering. In comparison with linear regression (LR), DT and DT-boosting neural network improved the coefficient of determination to 0.83. The authors of [18] used the long short-term memory (LSTM) architecture for forecasting solar panel generation. The achieved error of solar panel generation forecasting with a 15-min horizon was less than 5%. In article [19], forecast models for SPPs based on hourly measurements of various components of insolation, air temperature, wind speed and pressure were proposed for the day ahead based on SVR, DTs, DT ensembles, and swarm intelligence (Grey wolf optimisation). In study [20], for the hourly forecasting of solar irradiance for four years, models based on the LSTM, SVR and MLP algorithms were proposed, though the required parameters were first selected with the LASSO algorithm. The basic forecast models were additionally combined with the methods for quantile regression averaging and convex optimisation (COpt); the best base model was MLP, and the best combining algorithm was COpt. The authors of [21] proposed to forecast the generation of SPPs based on five years of data by neural networks; the Flocking algorithm was used to optimise the parameters of the resulting models, and the neural networks themselves were subsequently ensembled by bagging. As a result, the root mean squared error of the ensemble neural network (ENN) was 8% to 12% smaller relative to the basic models. Article [22] proposed short-term generation forecasting models based on SVR and extreme learning machine (ELM) algorithms: both models showed high accuracy (R\({}^{2}\) above 0.95), and the forecast error was 5-7%. Study [23] proposed to apply the k-means algorithm to cluster meteorological conditions and train a separate model for each cluster using a dataset of 35 years. The best result was obtained for MLP; the forecast error for an hour ahead was 8.6% with an R\({}^{2}\) of 0.96. A comprehensive review [10] of the use of machine learning methods for forecasting solar irradiance in various parts of the world showed that ensemble models and neural networks are the most popular tools used to solve this problem. For data pre-processing, clustering and models based on a mixture of Gaussian distributions are used. The forecast error for various planning horizons ranges from 3% to 20%. An analysis of existing works shows that the accuracy of solar irradiance forecasting is largely influenced by the composition and quality of meteorological data. Solar radiation on clear days can be predicted with high accuracy, even in the midterm [2; 23]. For cloudy days, the accuracy is significantly lower, even for a short-term forecast. Cloudiness introduces a significant difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and its value on the Earth's surface. The cloud cover can be very changeable; it complicates the task of solar irradiance forecasting. An important feature of cloudiness, in contrast to air temperature, wind speed, precipitation intensity and most other meteorological factors, is a much less formalised form of measurement. Clouds can have different densities and heights. The frequently used measure total cloud amount does not take this into account and only shows the percentage of the sky covered by any type of cloud. In most cases, at present and even more so in the past, cloud data are the results of visual subjective observation from the Earth's surface [24; 25]. This article proposes an algorithm for formalising cloud descriptions in natural language, thereby allowing them to be converted into categorical features suitable for the use of machine learning. In addition, it should be noted that at present, research in the field of forecasting SPP generation is still primarily aimed at increasing the accuracy of forecasts, while the models remain black boxes for users.