The deployment of Renewable Energy Communities (RECs) is a cornerstone of the transition toward low-carbon and resilient urban systems. Their effective planning requires decision-making tools capable of integrating environmental, social, and technical criteria within dynamic territorial and regulatory contexts. Traditional multi-criteria approaches (e.g., the Analytic Hierarchy Process) provide a solid methodological basis for site suitability analysis but are often limited by expert subjectivity, static weighting, and low adaptability to evolving conditions.

Artificial Intelligence (AI)-based tools could help at multiple stages of the decision-making process, from the automatic weighting of criteria to the optimization of alternative planning scenarios. Machine learning and deep learning algorithms can learn from historical data, such as energy performance records, socioeconomic indicators, or patterns of social acceptance, to calculate dynamic weights, thereby reducing dependence on subjective, judgment-based assessments. Adaptive multi-criteria systems can then update these weights in real time in response to regulatory changes, incentive availability, or variations in energy consumption. Natural Language Processing can further expand the decision base by extracting and classifying relevant information from complex sources, such as legislative documents, urban plans, and environmental impact reports, ensuring that planning processes remain aligned with current policies and sustainability goals. In parallel, optimization through AI metaheuristics, including genetic algorithms, swarm intelligence, or reinforcement learning, can identify optimal combinations of sites and configurations for RECs while balancing multiple constraints (e.g., landscape, historical, environmental, etc.). When integrated with Geographic Information Systems and remote sensing, these AI capabilities allow for continuously updated spatial analyses and transparent, explainable decision models.

The use of AI in energy planning offers a promising pathway to more adaptive, data-driven, and participatory frameworks for REC development, aligning local energy strategies with the broader objectives of circularity, sustainability, and resilience in the built environment.