Miguel Molina-Solana

The computational simulation of physical phenomena is a highly complex and expensive process. Traditional simulation models, based on equations describing the behavior of the system, do not allow generating data in sufficient quantity and speed to predict its evolution and make decisions accordingly automatically. These features are particularly relevant in building energy simulations. In this work, we introduce the idea of deep data-driven simulation models (D3S), a novel approach in terms of the combination of models. A D3S is capable of emulating the behavior of a system in a similar way to simulators based on physical principles but requiring less effort in its construction—it is learned automatically from historical data—and less time to run—no need to solve complex equations.

@inproceedings{mldads2021,
  title = {Towards data-driven simulation models for building energy management},
  author = {G\'omez-Romero, Juan and Molina-Solana, Miguel},
  booktitle = {Machine Learning and Data Assimilation for Dynamical Systems (MLDADS 2021)},
  year = {2021},
  month = jun,
  timestamp = {133},
  doi = {10.1007/978-3-030-77977-1_32},
  url = {}
}