This chapter examines the intersection of artificial intelligence and energy systems, addressing challenges such as distributed energy generation, grid flexibility, and building energy efficiency. A central focus is on a comprehensive and scalable architecture designed to facilitate the development and deployment of AI-driven models and solutions tailored to the energy sector. The proposed architecture comprises three interconnected layers: a data layer for robust acquisition and preprocessing, a model layer for training and optimizing intelligent models, and an application layer for delivering actionable insights through user-friendly interfaces. This architecture ensures streamlined data management, efficient model training, and accessible interpretation of results, addressing the diverse needs of stakeholders, including energy producers, grid operators, residential users, and policymakers. Additionally, the chapter introduces a methodological framework that spans all phases of AI model development, from data preprocessing to monitoring and deployment, emphasizing the significance of interpretability and scalability in delivering impactful energy solutions. By integrating advanced AI techniques with domain-specific knowledge, the framework and architecture collectively provide a roadmap for addressing current energy challenges and fostering innovation in energy management systems. This work serves as a valuable guide for researchers and practitioners seeking to leverage AI for sustainable and efficient energy solutions.