An efficient parallel implementation of a least squares problem

Least squares solutions are a very important problem, which appear in a broad range of disciplines (for instance, control systems, statistics, signal processing). Our interest in this kind of problems lies in their use of training neural network controllers.

An efficient parallel implementation of a least squares problem

Least squares solutions are a very important problem, which appear in a broad range of disciplines (for instance, control systems, statistics, signal processing). Our interest in this kind of problems lies in their use of training neural network controllers.

Estudo de soluções energéticas para a cabine de pintura automóvel

O processo de pintura de automóveis passa por várias fases que requerem grandes quantidades de energia térmica, uma vez que é necessário garantir uma temperatura mínima de aproximadamente 20ºC, na fase de aplicação, e de 60ºC, na fase de secagem. Pretende-se realizar uma auditoria energética a uma cabine de pintura automóvel, tendo em vista a optimização de todo o processo, desde a fase de aplicação até à fase de secagem. O objectivo principal deste trabalho consiste em encontrar alternativas energéticas ao usual gás propano que sejam economicamente viáveis. Assim, foram estudadas como alternativas as seguintes soluções: Opção 1 – Sistema solar térmico; Opção 2 - Sistema recuperação de calor (Extracção e Insuflação); Opção 3 – Sistema solar térmica mais recuperação de calor; Opção 4 – Sistema de recuperação de calor do compressor; Apresentadas a respectivas opções, realizaram-se então os estudos de viabilidade económica para cada solução.

Intelligent predictive control for thermal comfort and energy savings in public buildings

This talk addresses the problem of controlling a heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most operating conditions are conflicting goals requiring some sort of optimisation method to find appropriate solutions over time. In this work a discrete model based predictive control methodology is applied to the problem. It consists of three major components: the predictive models, implemented by radial basis function neural networks identifed by means of a multi-objective genetic algorithm [1]; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, with a special emphasis on a fast and accurate computation of the PMV indices [2]. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer [3] and winter [4] conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.