Energy profiling plays a crucial role in optimising smart building operations, especially with the increasing popularity of personalised, user-centric AI applications. Current research lacks emphasis on interpretability, transparency, and accessibility for non-expert stakeholders, where decision-making either relies solely on machine learning insights or unstructured knowledge bases. Hence, this study aims to enhance the interpretability of energy profiling and generate tailored recommendations based on correlated data sources from various aspects. This approach combines data-driven and knowledge-driven techniques by integrating energy clustering insights and unstructured knowledge bases to provide tailored energy recommendations. By combining Large Language Models (LLMs) and Explainable AI (XAI), this approach leads to: (1) identifying new consumer personas based on contextualised cluster insights, (2) finding the most impactful features reflecting energy insights, and (3) turning those insights into clear, human-readable reports and recommendations. This transforms smart meters from passive data collectors into intelligent advisory tools for consumers, policymakers, and energy providers.