A large part of French electricity consumption variation is due to temperature fluctuations. While HVAC (heating, ventilation and air-conditioning) systems consumption are directly affected by the temperature, other systems (refrigerator, freezer, water heater) can also be driven by weather changes making thermal contribution to overall consumption difficult to extract. This paper presents a ''by-design'' unsupervised data-driven method to separate the consumptions due to the weather in the overall electricity consumption. The proposed deep learning model is based on the separation of meteorological parameters from calendar ones within the model architecture. The performances of this model, in particular its ability to split consumption mechanisms, is tested on a synthetic dataset and on the french consumption dataset. Being relatively simple and interpretable, this approach can be generalized to other countries whereas energy sobriety represents an important challenge we are facing.