Enhancing energy control stability under extreme conditions by integrating weather forecasts into ARLEM
Increasing the stability of energy management systems is crucial, especially when facing extreme weather events. A common approach to prevent sudden oscillations is the implementation of predictive models; however, this often requires more complex models and greater computational power. In this work, weather forecasts are integrated into an approach based on Adaptive Reinforcement Learning for Ene
