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The ARIREF (Adaptive Reflective Intelligence for Renewable Energy Forecasting) model represents a conceptual approach designed to enhance the accuracy and efficiency of renewable energy source forecasting. Based on a comprehensive review of scientific research, the model proposes an iterative modelling method that integrates adaptive and self-reflective artificial intelligence technologies. These technologies enable the model to continuously adapt and learn from changing conditions, thereby improving forecasting accuracy and performance. The ARIREF model is distinguished by its self-improvement cycle, providing a bidirectional dynamic enhancement process. This cycle effectively utilizes feedback to optimize algorithms and methods. It allows the model to learn from past mistakes and proactively make improvements, creating an iterative learning process. These adaptive and self-improvement capabilities are crucial for effectively addressing the complexities and variabilities of renewable energy forecasting. The main findings of the study highlight the ARIREF model’s theoretical potential to facilitate the integration of renewable energies into broader energy systems, offering a crucial contribution to global sustainability efforts. As the model is still in the conceptual stage, this study emphasizes the need for further research. Such research is necessary to validate and refine ARIREF theoretical constructs, ensuring its applicability and impact on sustainable energy supply. The study reveals the necessity for innovative and adaptive solutions in the domain of renewable energy forecasting to overcome current methodological limitations and meet the increasing demands for precise and reliable energy source predictions.
Veigners et al. (Wed,) studied this question.