A review on methods for long-term forecasting of RES installed capacity

The widespread adoption of renewable energy production signals a fundamental transformation in global energy dynamics, underlining the need for a sustainable energy transition. As solar and wind energy sources continue to expand, the need to integrate these technologies into existing energy infrastructures becomes increasingly urgent. European and national regulations advocate for the adoption of renewable energy necessary to reach environmental sustainability and further economic development. However, challenges such as grid stability and environmental impact necessitate careful management of renewable energy deployment. Forecasting the installation of RES emerges as a key point, guiding strategic decision-making and resource allocation. This paper reviews and classifies forecasting models utilized in recent years, taking into consideration traditional methods and the expanding application of artificial intelligence (AI) tools. By synthesizing empirical research and theoretical frameworks, the review offers insights into the current landscape of renewable energy forecasting and underscores the importance of accurate forecasting methodologies to obtain optimal deployment strategies. Through critical analysis and classification, the paper elucidates the strengths, limitations, and potential applications of different forecasting approaches.