António E. Ruano

TL;DR: In this paper, a discrete model-based predictive control methodology is applied, consisting of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm; the cost function that will be optimised to minimise energy consumption and maintain thermal comfort; and the optimisation method, a discrete branch and bound approach.

TL;DR: The design of inside air temperature predictive neural network models, to be used for predictive control of air-conditioned systems, is discussed and the performance of these data-driven models is compared, favourably, with a multi-node physically based model.

TL;DR: A feature detection system for real-time identification of lines, circles and people legs from laser range data is developed and a new method suitable for arc/circle detection is proposed: the Inscribed Angle Variance (IAV).

TL;DR: Off-line method exploits the linear–non-linear structure found in radial basis function neural networks to model the inside air temperature of a hydroponic greenhouse and its application to on-line learning is proposed.

TL;DR: A detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions as mentioned in this paper.