The increasing reliance on renewable energy sources such as solar and wind power necessitates the development of advanced forecasting techniques to address the inherent variability and unpredictability of these energy systems. Accurate forecasting is vital for optimising energy production, maintaining grid stability, and effectively integrating renewable energy into power systems. Traditional forecasting methods often struggle to adapt to rapidly changing environmental conditions and new data inputs, limiting their effectiveness in dynamic contexts. This study introduces the Self-Improvement Cycle (SIC) module, which is designed to enhance forecasting accuracy through continuous learning, adaptation, and feedback integration. The SIC module leverages advanced machine learning algorithms, reinforcement learning techniques, and reflective practice principles to create a self-improving framework that dynamically updates models based on real-time data and external feedback. The module’s design incorporates multiple feedback loops, enabling the system to iteratively refine its performance and remain robust in the face of changing conditions. Reflective practice, a concept drawn from psychology, plays a critical role in the SIC module by facilitating ongoing evaluation and adaptation. By learning from previous predictions and continuously adjusting algorithms, the SIC module demonstrates its potential to improve forecasting accuracy across various domains, with a particular emphasis on renewable energy forecasting. The theoretical and mathematical foundations of the SIC module are explored, showcasing its capability to enhance predictive accuracy and resilience in an evolving energy landscape.

The European Union’s Green Deal agenda emphasises the major importance of cleaner and more environmentally friendly energy sources for further economic development. Hydrogen is one of the potential renewable fuels that can replace fossil fuels in a variety of economic applications. The green hydrogen production across Europe is a key factor in achieving this goal. The aim of the study is to determine the influence of the connection between the availability of renewable energy sources, innovation, and economic development patterns on hydrogen production advancement. The research provides a comprehensive correlation analysis between the number of green hydrogen production sites and their capacity with statistical indicators describing economic growth, population, innovation, and particular economic characteristics within a particular EU Member State. The statistical data of this research outline the period from 2021 until the beginning of 2024. The results of the study suggest that nine of the 23 indicators examined – population size, gross domestic product, investment in research and development, total manufacturing value, overall industry value, total electricity produced, renewables in electricity production, solar power, and wind power in electricity production – have a strong or very strong positive correlation with hydrogen production. Furthermore, the analyses revealed that countries with large populations and high GDP volumes are more likely to develop hydrogen production in existing market circumstances.

Energy production, supply and consumption are global issue with many economic, environmental and social implications. Mentioned issue is even more expressed in remote rural areas, in particular in developing countries, as are the countries of the Western Balkans (WB). Renewable energy sources (RES) could represent optimal energy alternative for sustainable performing of agricultural and other activities, as well as for improving the current state of living conditions in rural communities. The main goal of research is to mark the most suitable RES alternative (six alternatives) for wider implementation in rural space of WB. The applied methodology framework implies experts’ opinion (engagement of eight experts) and the use of multi-criteria decision-making methods (MCDM), (specifically fuzzy-rough LMWA and fuzzy-rough CRADIS methods) under the predefined criteria (nine criteria). Derived results show that the implementation of the solar energy plants could play an optimal solution, while as the relatively unsuitable alternative could be marked the use of energy potential of watercourses. Gained final result, i.e. ranking order of the considered alternatives is additionally verified by the appliance of other MCDM methods, while the sensitivity analysis was also performed.