# Control of Renewable Energy Communities using AI and Real-World Data

arXiv: https://arxiv.org/abs/2505.17321 · HTML: https://mlsanity.com/paper/2505.17321/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 22 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 8 of 8)

- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [Controlling Commercial Cooling Systems Using Reinforcement Learning](https://mlsanity.com/paper/2211.07357/) (2022)
- [CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities](https://mlsanity.com/paper/2405.03848/) (2024)
- [Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings](https://mlsanity.com/paper/2112.06127/) (2021)
- [EVLearn: Extending the CityLearn Framework with Electric Vehicle Simulation](https://mlsanity.com/paper/2404.06521/) (2024)
- [EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management](https://mlsanity.com/paper/2404.02361/) (2024)
- [A Relearning Approach to Reinforcement Learning for Control of Smart Buildings](https://mlsanity.com/paper/2008.01879/) (2020)
- [How Can Energy Communities Provide Grid Services? A Dynamic Pricing Mechanism with Budget Balance, Individual Rationality, and Fair Allocation](https://mlsanity.com/paper/2309.05363/) (2023)

## Built on by (most cited first, 1 of 1)

- [Percepta: High Performance Stream Processing at the Edge](https://mlsanity.com/paper/2510.05149/) (2025)

## Abstract (arXiv)

The electrification of transportation and the increased adoption of decentralized renewable energy generation have added complexity to managing Renewable Energy Communities (RECs). Integrating Electric Vehicle (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), particularly MultiAgent Deep Deterministic Policy Gradient (MADDPG) algorithms, have 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-reality 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 behaviors. These outcomes underscore the framework's effectiveness, advancing the practical deployment of intelligent energy management solutions in RECs.