Opportunities and Challenges of Using Artificial Intelligence in Energy Communities

Energy communities are legal entities that produce, store, and sell renewable energy (RE) while also exchanging it inside the community via the public grid. They provide economic, environmental, and social advantages to the community, but they also confront enormous obstacles in anticipating, managing, and participating in energy markets. This paper explores the potential of artificial intelligence (AI) in addressing the challenges faced by energy communities. AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic (FL), neural networks (NNs), and genetic algorithms (GAs), can enhance forecasting, optimization, and control of renewable energy sources (RES) and demand within energy communities. The paper discusses the concept of energy communities, the role of AI, and its applications in predicting energy generation, optimizing grid operations, managing energy storage, controlling devices, and facilitating energy trading. However, barriers such as data availability, costs, ethics, regulations, and skills gaps hinder AI adoption. Overcoming these barriers requires policy development, collaboration, data security, ethical AI use, and education. According to the research, AI could assist energy communities in developing a more sustainable and resilient energy system.