Neural Network for Predicting the Energy Performance of a Building

I spent years, during my master's studies in engineering, trying to model energy systems. In most cases, only the simplest problems can be modelled directly (analytical resolution of the governing differential equations of the system studied), for particularly simple and convenient geometries and boundary conditions. More complex problems are tackled using various mathematical/numerical or procedural techniques to simplify their nature so that reasonably accurate, albeit approximate, calculation models can be developed. One of the most interesting aspects of my recent experience with the integration of Deep Learning and engineering in the broadest sense (structural analysis, fluid dynamics, energy systems…) is the possibility of approaching the problems studied in completely different ways. The abundance and complexity of data is no longer a problem, but an advantage, allowing more accurate and sophisticated forecasting models to be developed.