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%.

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.

Root ferric chelate reductase is regulated by iron and copper in straberry plants

In the present experiment, we studied the interaction between copper (Cu) and iron (Fe) in strawberry plants grown in nutrient solutions containing different concentrations of Fe. Plants grown in the absence of iron (Fe0) had the characteristic symptoms of Fe deficiency, with smaller chlorotic leaves, less biomass, acidification of the nutrient solution, and roots that were smaller and less ramified, while no symptoms of Fe deficiency were observed in plants grown with Fe. A greater amount of Cu was found in roots of chlorotic plants than in those grown with Fe, while plants grown with 20M of Fe (Fe20) in the nutrient solution had a greater amount of Fe compared with plants from the other treatments. Chlorotic plants (Fe0) and plants grown with the greatest level of Fe (Fe20) had a greater root ferric chelate reductase (FC-R; EC 1.16.1.17) activity compared with the other treatments with 5 or 10M Fe in the nutrient solution. The same pattern was obtained for relative FC-R mRNA concentration and for the sum of Fe and Cu contents in shoots (leaves plus crowns). The DNA obtained from amplification of the FC-R mRNA was cloned and several of the inserts analysed by single strand confirmation polymorphism (SSCP). Although there were different SSCP patterns in the Fe20 treatment, all the inserts that were sequenced were very similar, excluding the hypothesis of more than one FC-R mRNA species being present. The results suggest that Cu as well as Fe is involved in FC-R expression and activity, although the mechanism involved in this regulation is unknown so far. Both small contents of Fe and Cu in plants led to an over-expression of the FC-R gene and enhanced FC-R activity in strawberry roots.

Predictive control of HVAC systems in buildings

Dissertação de Mestrado, Engenharia Eletrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015