The Importance of Renewable Energy Forecasting for the Development of a Sustainable Energy Ecosystem

This article aims to explore major trends and influences in the field of renewable energy forecasting, applying a complex methodological approach that combines a literature analysis with detailed econometric evaluation. The methodology included the analysis of various articles retrieved from leading international databases, revealing a growing interest in the use of machine learning algorithms and neural networks for solar and wind energy forecasting. A growing emphasis on machine learning algorithms and neural networks for solar and wind energy forecasting is observed, underscoring the transition toward more sophisticated prediction tools. The econometric analysis investigates time series data related to installed renewable energy capacity and electricity generation over the 2010–2020 period. The results revealed a steady upward trend in installed capacity worldwide, increasing from 2010 to in 2020, as well as in energy production. Significant seasonal fluctuations and residual factors suggesting unforeseen external influences were also identified. These findings highlight the importance of integrating complex predictive technologies into energy management strategies to effectively address the variability of renewable resources and ensure the stability of energy grids.