The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model.

Obtaining wind energy for the production of electric energy plays a key role in overcoming the problems associated with climate change and the dwindling reserves of traditional types of energy resources. The purpose of this work is to analyze current methods of energy estimation and forecasting, to consider the main classifications of forecasts and methods used in their construction and to review the main types of mathematical distributions used to calculate the speed and power of wind flow, depending on specific geographical conditions. In recent years, there has been an increase in the capacity of modern wind generators, which has significantly improved the efficiency of wind energy parks. The initial stage in determining the feasibility of involving a particular energy source in the overall energy system of the region is a preliminary assessment of the energy potential, allowing one to determine the possible percentage of substitution of traditional energy. To solve such a problem, it is necessary to use models of energy supply. Evaluation of wind as a resource creates certain difficulties in modeling because this resource is stochastic and variable. In this regard, this paper proposes to consider various models for estimating wind energy potential, which can be classified into empirical models and models based on the application of modern intelligent data analysis technologies. The paper presents an analysis of the existing models for estimating the amount of energy, which can be used in a system designed to determine the most optimal configuration of the energy system based on the use of different conversion technologies most relevant to the case under study, and it also serves as the basis for creating digital twins designed to model and optimize the operation of the projected energy complex.

Energy production based on renewable sources is a fundamental aspect of society’s sustainable development. The involvement of renewable energy sources in the implementation of modern energy systems can significantly reduce the amount of harmful emissions into the atmosphere and provide greater flexibility of energy infrastructure. The first step in determining the feasibility of involving a particular energy source in the overall energy system of the region is a preliminary assessment of the energy potential to determine the possible percentage of substitution of traditional energy. To solve this problem, it is necessary to use the models of energy supply, which are currently presented in a wide variety. In this regard, this paper proposes to consider various models for estimating the solar energy potential, which can be divided into empirical models and models based on the application of modern intelligent data analysis technologies. Such models are based on many different climatic and geographical indicators, such as: longitude of sunshine, ambient temperature, serial number of the day of the current year, amount of precipitation, average and maximum values of wind speed and so on. The paper analyzed the existing models for estimating the amount of energy, which can be used in the system designed to determine the most optimal configuration of the energy system based on the use of various conversion technologies most relevant to the case under study, and also serve as the basis for creating digital twins designed to model and optimize the operation of the projected energy complex

In this paper a review of existing software tools for the evaluating and modeling of renewable energy sources, which allow to determine various aspects, such as: the amount of incoming energy for a particular area, the economic feasibility of involving an alternative source, the impact on the environment, the cost of the projected system, the features of the terrain, etc. Programs for the evaluation of solar and wind energy, as well as solutions allowing the evaluation of several different types of renewable energy sources were considered. As a result, open-source software tools, such as: QGIS, SAGA GIS, which can be integrated into the implementation of a comprehensive assessment of renewable energy sources to perform certain functions, such as a preliminary assessment of the gross renewable energy potential in specific geographical conditions and identifying topographical features of the regions under consideration, were defined.

This article discusses the process of creating a software and hardware complex for obtaining meteorological data, based on which it is possible to draw up recommendations for choosing the most optimal energy supply system using renewable energy sources. A method for obtaining data on wind speed and direction has been implemented, a methodology has been considered for processing these data to assess the potential of wind energy for a particular area. The Arduino microcontroller and corresponding measuring devices are used to obtain, collect and convert data. A software module that calculates the value of incoming solar radiation at a specific location of the consumer is considered. The use of this complex will allow evaluating the feasibility of involving renewable energy sources in the consumer's energy system.

In the article an approach to accounting uncertainty of the initial information in the system research. It consists in the inclusion of uncertainty as one of the parameters in the studied model systems. The authors propose classification according to the degree of uncertainty, which allows you to identify the following types of uncertainty: complete certainty, stochastic and fuzzy uncertainty. Described mathematical apparatus for recording and eliminating uncertainty.