  Digital transformation in the built environment generates vast data for
developing data-driven models to optimize building operations. This study
presents an integrated solution utilizing edge computing, digital twins, and
deep learning to enhance the understanding of climate in buildings. Parametric
digital twins, created using an ontology, ensure consistent data representation
across diverse service systems equipped by different buildings. Based on
created digital twins and collected data, deep learning methods are employed to
develop predictive models for identifying patterns in indoor climate and
providing insights. Both the parametric digital twin and deep learning models
are deployed on edge for low latency and privacy compliance. As a
demonstration, a case study was conducted in a historic building in
\"Osterg\"otland, Sweden, to compare the performance of five deep learning
architectures. The results indicate that the time-series dense encoder model
exhibited strong competitiveness in performing multi-horizon forecasts of
indoor temperature and relative humidity with low computational costs.
