Today, energy consumption in the world is growing and it is becoming urgent to solve the problem of replacing traditional energy sources with alternative ones. The solution to this problem is impossible without a preliminary data analysis and further forecasting of energy production by alternative sources. However, the use of alternative energy sources in the conditions of the wholesale electricity and capacity market currently operating on the territory of the Russian Federation is impossible without the use of short-term predictive “day ahead” models. In this article, the authors perform a brief analysis of the existing methods of short-term forecasting which are used when making forecasts for the generation of electricity by solar power plants. Currently, there are already a fairly large number of predictive models built within each of the selected methods of short-term forecasting, and they all differ in their characteristics. Therefore, in order to identify the most promising method of short-term forecasting for further use and development, the authors used a previously developed classification. In the course of the study, a preliminary processing of initial data obtained from the existing solar power plants using spectral analysis was carried out. Further, to build a predictive model, a correlation analysis of the initial data was carried out, which showed the absence of a linear relationship between the components in the retrospective data. Based on the results of the correlation analysis the authors made a decision to select parameters empirically in order to build a predictive model. As a result of the study, a mathematical model based on an artificial neural network was proposed and a learning sample was generated for it. In addition, the architecture of an artificial neural network was determined, the result of which is a short-term forecast of electric power generation in the "day ahead" mode, and calculations were performed to obtain numerical values of the forecast. From the results of the study, it follows that the developed predictive model in the predicted interval has a mean absolute error of about 13.5 MW. However, at some intervals, the peak discrepancies can reach up to 200 MW. The root mean square error of the model is 27.8 MW.

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Nowadays, energy consumption in the world is growing becomes relevant to solve the problem of replacing traditional sources with alternative ones. The solution to this problem is impossible without preliminary forecasting of energy production by alternative sources. In this paper, we consider the problem of solving the problem forecasting electric energy by solar power plants, considering the influence of external factors (weather conditions) using a model implemented on the basis a neural network.

As part of this work, a simulation model was constructed to evaluate the print quality of three-dimensional objects using a 3D printer. A study was conducted using non-contact scanning of a 3D model and varying the parameters affecting print quality, followed by an assessment of the degree to which the printed 3D model corresponded to its standard using software. The result of this study is the obtained values of the optimal printing parameters of the 3D model, which allow to obtain a high estimate of the degree of correspondence of the printed 3D model to its standard, and, consequently, high quality printing on a 3D printer.