In recent years, decentralized renewable energy production has gained increasing importance. Challenges of distributed energy production include fluctuations in weather-dependent energy generation, which may not always meet peak consumption periods and can result in significant overproduction during low-load periods. Managing production and consumption is a fundamental task for efficient renewable energy utilization. The application of lithium-ion or other advanced battery technologies as community energy storage provides a more reliable power supply when operated optimally with advanced energy management and control systems. Digital platforms form the basis of Energy Communities, supporting necessary processes and functionalities, and enabling the integration of smart grids that utilize data from IoT devices, meteorological sources, and energy markets. This paper presents a design of an Energy Community management platform and digital tools that provide a systematic framework for mapping energy consumption trend within energy communities. Adopting a digital platform for an energy community involves the integration of IoT devices, a centralized database, and a software platform equipped with AIbased forecasting tools. Additionally, investigations into various modeling approaches have highlighted the superior performance of hybrid deep learning models, specifically those combining GRU and LSTM architectures, in predicting energy consumption. These models excel in forecasting consumption peaks, which is crucial for optimizing energy distribution and storage within the community and are able to overcome the limitations of classical forecasting methods, which usually do not account for external variables like weather changes, consumer trends, and technological advancements that might affect energy use.