Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context.

The energy consumption of a building is significantly influenced by the habits of its occupants. These habits not only pertain to occupancy states, such as presence or absence, but also extend to more detailed aspects of occupant behavior. To accurately capture this information, it is essential to use tools that can monitor occupant habits without altering them.

Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks.

With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption.

Evaluation of a graphic interface to control a robotic grasping arm: a multicenter study.

Unlabelled: Laffont I, Biard N, Chalubert G, Delahoche L, Marhic B, Boyer FC, Leroux C. Evaluation of a graphic interface to control a robotic grasping arm: a multicenter study.

Objective: Grasping robots are still difficult to use for persons with disabilities because of inadequate human-machine interfaces (HMIs).