The use of unmanned aerial vehicles (UAVs) in various spheres of human activity is a promising direction for countries with very different types of economies. This statement refers to resource-rich economies as well. The peculiarities of such countries are associated with the dependence on resource prices since their economies present low diversification. Therefore, the employment of new technologies is one of the ways of increasing the sustainability of such economy development. In this context, the use of UAVs is a prospect direction, since they are relatively cheap, reliable, and their use does not require a high-tech background. The most common use of UAVs is associated with various types of monitoring tasks. In addition, UAVs can be used for organizing communication, search, cargo delivery, field processing, etc. Using additional elements of artificial intelligence (AI) together with UAVs helps to solve the problems in automatic or semi-automatic mode. Such UAV is named intelligent unmanned aerial vehicle technology (IUAVT), and its employment allows increasing the UAV-based technology efficiency. However, in order to adapt IUAVT in the sectors of economy, it is necessary to overcome a range of limitations. The research is devoted to the analysis of opportunities and obstacles to the adaptation of IUAVT in the economy. The possible economic effect is estimated for Kazakhstan as one of the resource-rich countries. The review consists of three main parts. The first part describes the IUAVT application areas and the tasks it can solve. The following areas of application are considered: precision agriculture, the hazardous geophysical processes monitoring, environmental pollution monitoring, exploration of minerals, wild animals monitoring, technical and engineering structures monitoring, and traffic monitoring. The economic potential is estimated by the areas of application of IUAVT in Kazakhstan. The second part contains the review of the technical, legal, and software-algorithmic limitations of IUAVT and modern approaches aimed at overcoming these limitations. The third part—discussion—comprises the consideration of the impact of these limitations and unsolved tasks of the IUAVT employment in the areas of activity under consideration, and assessment of the overall economic effect.

This study provides a detailed analysis and prediction of power generation at wind farms in Germany using Lasso, LightGBM, and CatBoost machine learning models. Feature Engineering was used on the data, which allowed the extraction of more detailed data, which was used to improve the quality of the models. Through Extensive Data Analysis (EDA), the authors identify and develop lagged and moving features from the energy production time series, under the assumption that accurate predictions can significantly improve the stability of energy systems, especially in the context of increasing dependence on renewable energy sources. The performance of each model is evaluated based on the Mean Absolute Error(MAE), Mean Squared Error(MSE), and Root Mean Squared Error(RMSE) metrics, with CatBoost exhibiting the highest accuracy. In conclude, pointing to opportunities for further research aimed at optimizing these models and adapting them to other regions, emphasizing the comprehensive and long-term potential of this study in the context of energy field.