The potential energy savings resulting from the cooperative management of community districts, also known as energy hubs, has been widely demonstrated throughout the literature. Various developed predictive control strategies have proven to generate remarkable gains by exploiting the shared energy generation, conversion and storage devices of building communities. However, one difficulty amongst these methods lie in the integration of information regarding the long term perturbations brought to the system. While most of the existing work considers prediction horizons ranging from day ahead to weekly time frames, recent studies have explored the control of inter-seasonal storage units on a yearly basis through available historical data sets. This study focuses on testing and improving the existing methods while integrating combined and stand alone wind generation to the system. In particular, the contribution of the seasonal storage state bounds for Value function approximation and control process has been investigated for several well-known methods, including scenario approach, stochastic dual dynamic programing and adaptive dynamic programing. Solving the resulting multistage stochastic optimization problem reveals very good results and demonstrate the contributions of the bounds to almost all Value function approximation methods. In fine, the integration of wind production to the system underlines the importance of seasonal trends for efficient optimization of long term storage and suggests using tolerance margins around the bounds for systems particularly sensitive to perturbations.