We have long-standing experience in applying big data and AI to energy simulation and management, leveraging the power of web services and monitoring solutions in residential, commercial, and industrial buildings to optimize consumption.

Buildings generate increasingly heterogeneous and raw data from multiple sources. When properly collected, harmonised and analysed, this data enables smarter use of assets: greater energy efficiency, improved occupant comfort and better design and refurbishment decisions. Our work focuses on end-to-end data pipelines—from ingestion to model training—that support policy evaluation and large-scale analysis of building stocks using robust and comparable KPIs.

We design tools that integrate ontological alignment and semantic web principles (RDF/OWL, shared vocabularies and linked data) to standardise meanings across systems and ensure full interoperability. This semantic scaffolding allows us to merge BMS/SCADA, IoT, maintenance and contextual data, reducing uncertainty and making results reusable across projects and cities.

These tools have been developed and validated over many years in international projects such as EDI_Net and SHERPA, and in collaboration with industry through the spin-off Inergy.

In addition, BEE Group develops building energy models and management strategies, along with web services and monitoring tools, to analyse the actual energy performance of large building portfolios.