Abstract: A computer-implemented system and method for automatically improving gathering of data includes processing first input data acquired by a data gathering device to generate a first prediction and a corresponding confidence score. If the confidence score is below a threshold, then generating and applying a set of at least one action to the data gathering device, such as changing acquisition settings of the device. Second input data acquired using the data gathering device having the action applied thereto is further received and processed to generate a second prediction and corresponding confidence score. The set of action to-be-applied to the device is further modified based on the difference. The generation of the at least one action, such as changing the acquisition settings, can be based on an acquisition settings adjustment model and the modification of the action can include updating the model by machine learning (ex: reinforcement learning).

Abstract: A joint utility predictor and controller (JUPAC) system would allow a utility such as an energy supplier and its consumers to better predict electricity grid activity and then, optimize its energy production, management, distribution, and consumption. The more accurate the prediction the more positive its economic and environmental impacts will be. A JUPAC system at a consumer collects ambient parameters, user patterns, and energy usage before by exploiting an embedded machine learning algorithm it predicts the consumer's future consumption This prediction may be recurrently transmitted to the energy supplier as a formatted commitment then, in a second time, the same device will try to respect this commitment by adjusting, wisely, the user appliances and heating—ventilation and air conditioning.

Abstract: A computer-implemented system and method for automatically improving gathering of data includes processing first input data acquired by a data gathering device to generate a first prediction and a corresponding confidence score. If the confidence score is below a threshold, then generating and applying a set of at least one action to the data gathering device, such as changing acquisition settings of the device. Second input data acquired using the data gathering device having the action applied thereto is further received and processed to generate a second prediction and corresponding confidence score. The set of action to-be-applied to the device is further modified based on the difference. The generation of the at least one action, such as changing the acquisition settings, can be based on an acquisition settings adjustment model and the modification of the action can include updating the model by machine learning (ex: reinforcement learning).