The Horizon2020 Smart4RES project develops forecasting and optimization solutions for the operation of Renewable Energy Sources (RES) and their application in electricity market trading and grid management. As a follow-up of the Poster presented in 2021, this poster presents the latest solutions developed in Smart4RES that intend to cover a large spectrum of prediction horizons and spatial scales in order to maximize the interest for the wind power industry. At the scale of a wind farm, the fluctuations of wind conditions at very-short-term horizons (seconds to minutes ahead) impose significant variations in the structural load of the turbine and its power output. These variations are challenging for a precise control of wind turbines. The forecasting models developed by Smart4RES make use of new data sources unexploited by traditional models such as 2-beam and 4-beam nacelle-mounted LIDAR. Results obtained on an operating turbine equipped with LIDAR show significant improvement in the forecasting error of both structural load and active power output for the next 20 seconds ahead. At the horizon of the next minutes, the power output of a wind farm is impacted by weather variability but also by wake losses that may vary as a function of turbine curtailment following a system operator request. This is why Smart4RES proposes a dynamic Machine Learning (ML) prediction model based on Transfer Learning which provides adaptive forecast that beat state-of-art approaches including a similar ML model trained only in batch mode. Aggregations of renewable power plants are key players for renewable-based provision of services to the grid and optimized management of distribution grids. In this context, a coherent forecast over the entire hierarchy of the aggregation is essential in order to take decisions that are feasible considering local constraints in the various levels of the hierarchy. A main challenge in such hierarchies is to produce a forecast even if data is missing, which can occur frequently at different periods and levels in the hierarchy. Whereas existing approaches tend to discard periods with missing data, which can drastically reduce the amount of available data for training and the applicability of the forecasting model in real conditions, Smart4RES proposes an end-to-end learning approach that is able to derive coherent and precise hierarchical forecasts even in the presence of missing values at different levels of the hierarchy.