Clarisse Sousa, Tiago Fonseca, Luis Lino Ferreira, Ricardo Venâncio, Ricardo Severino INESC - TEC/ Instituto Superior de Engenharia do Porto Porto, Portugal {cassa, calof, llf, ravrf, sev } @isep .ipp.pt Abstract -- The rise of real - time data and the proliferation of Internet of Things (IoT) devices have highlighted the limitations of cloud - centric solutions, particularly regarding latency, bandwidth, and privacy. These challenges have driven the growth of Edge Computing. Associated with IoT appears a set of other problems, like: d ata rate harmonization between multiple sources, protocol conversion, handling the loss of data and the integration with Artificial Intelligence ( AI) models . This paper presents Percepta, a lightweight D ata S tream P rocess ing (DSP) system tailored to support AI workloads at the edge, with a particular focus on such as Reinforcement Lear ning (RL). It introduces specialized features such as reward function computation, data storage for model retraining, and real - time data preparation to support continuous decision - making. Additional functionalities include data normalization, harmonization across hetero geneous protocols and sampling rates, and robust handling of missing or incomplete data, making it well - suited for the challenges of edge - based AI deployment .

-- The electrification of transportation and the increased adoption of decentralized renewable energy generation have added complexity to managing Renewable Energy Communities (RECs). I ntegrating E lectric V ehicle (EV) charging with building energy systems like heating, ventilation, air conditioning (HVAC), photovoltaic (PV) generation, and battery storage presents significant opportunities but also practical challenges. Reinforcement learning (RL), particula rly Multi - Agent Deep Deterministic Policy Gradient (M ADDPG) algorithms, ha ve shown promising results in simulation, outperforming heuristic control strategies. However, translating these successes into real - world deployments faces substantial challenges, including incomplete and noisy data, integration of heterogeneous subsystems, synchronization issues, unpredictable occupant behavior, and missing critical EV state - of - charge (SoC) information. This paper introduces a framework designed explicitly to handle these complexities and bridge the simulation - to - real ity gap. The framework incorporates EnergAIze, a MADDPG - based multi - agent control strategy, and specifically addresses challenges related to real - world data collection, system integration, and user behavior modeling. Preliminary results collected from a real - world operational REC with four residential buildings demonstrate the practical feasibility of our approach, achieving an average 9 % reduction in daily peak demand and a 5% decrease in energy costs through optimized load scheduling and EV charging behav iors. These outcomes underscore the framework's effectiveness, advancing the practical deployment of intelligent energy management solutions in RECs. Modern smart buildings and energy communities are increasingly integrating distributed energy resources (DERs) such as solar photovoltaics (PV), battery storage, and electric vehicle (EV) charging infrastructure. Collectively, buildings account for approxi mately 32% of global energy consumption and 34% of energy - related CO emissions, underscoring their pivotal role in climate mitigation efforts [1] .