Abstract
Reducing the gap between renewable energy needs and supply is crucial to achieve sustainable growth. Hydroelectric power production predictions in several Madeira Island catchment regions are shown in this article using Long Short-Term Memory, LSTM, networks. In order to foresee hydro reservoirs inflows, our models take into account the island's dynamic precipitation and flow rates and simplify the process of water moving from the cloud to the turbine. The model developed for the Socorridos Faja Rodrigues system demonstrates the proficiency of LSTMs in capturing the unexpected flow behavior through its low RMSE. When it comes to energy planning, the model built for the CTIII Paul Velho system gives useful information despite its lower accuracy when it comes to anticipating problems.

Abstract
While the share of renewable energy in intercon-nected systems has been increasing steadily, in isolated systems it represents a bigger challenge. This paper presents a dispatch algorithm integrating thermal, wind, solar and hydro generation and storage for an isolated network, which allows maximizing renewable energy integration and reducing the share of thermal energy in the mix. The possibility of using the battery to provide 'spinning' reserve is also considered. The algorithm was tested and validated using real data from the island of Madeira, Portugal. Results prove the robustness and flexibility of the algorithm, showing that a significant decrease in the thermal fraction is achievable, and that it is possible to accommodate an increase in renewable generation with minimal or no curtailment at all.