This paper deals with energy efficiency in buildings in order to mitigate the climate change. Buildings are the highest source of energy consumption worldwide. However, a large part of this energy is wasted, mainly due to poor buildings management. Therefore, being accurately informed about consumptions and detecting anomalies are essential steps to overcome this problem. Currently, some software exist to typically record, store, archive, and visualize big data such as the ones of a building, a campus, or a city. Yet, they do not provide Artificial Intelligence (AI) able to automatically analyze the streaming data to detect anomalies and send alerts, as well as adapted reports to the different stakeholders. The system designed in the sandfox project has for objective to fill this gap. To improve the energy management, an innovative system should aim at visualizing the streaming data, editing reports, and detecting anomalies, for different stakeholders, such as policy makers, energy managers , researchers, technical staff or end-users of these buildings. The paper presents the User-Centred Design approach that was used to collect the required needs from different stakeholders. The developed AI system is called sandman (semi-Supervised ANomaly Detection with Multi-AgeNt systems). It processes data in a time constrained manner to detect anomalies as early as possible. sandman is based on the paradigm of self-adaptive multi-agent systems. The results show the robustness of the AI regarding the detection of noisy data, of different types of anomalies, and the scaling.