Using Model Predictive Control to Modulate the Humidity in a Broiler House and Effect on Energy Consumption

In moderate climate, broiler chicken houses are important heating energy consumers and hence heating fuel consumption accounts for a large part in operating costs. They can be reduced by constructional measures, which in turn lead to important costs as well. On the other hand, a software solution to reduce energy would lead to considerably less follow-up costs. The main objective of our work was to assess if it is possible to save energy with a software solution and eventually quantify the savings for a given broiler house in the Swiss Plateau. The investigation was carried out in simulation: the particular broiler house was measured, and a dynamical model for it was derived and validated. To actually search for a particular behaviour of the software that would lead to energy savings, model predictive control was used. The idea was not to specify a particular behaviour of the software but rather to let the software itself find the best behaviour in an exhaustive search. The simulations showed that energy savings can be realised mainly by letting the indoor humidity deviate from what usually is used as setpoint and hence take profit of the outdoor climate, which changes naturally during a 24-hour course. We used expert opinions to determine how long and large these setpoint deviations may be without harming the broilers. The simulations showed alsothat the light control and the biological activity of the animals reduced the potential savings

Approximately periodic time series and nonlinear structures

In this thesis a previously developed framework for modelling diversity of approximately periodic time series is considered. In this framework the diversity is modelled deterministically, exploiting the irregularity of chaos. This is an alternative to other well established frameworks which use probability distributions and other stochastic tools to describe diversity. The diversity which is to be modelled, on the other hand, is not assumed to be of chaotic nature, but can stem out from a stochastic process, though it has never been verified before, whether or not purely stochastic patterns can be modelled that way. The main application of such a modelling technique would be pattern recognition; once a model for a learning set of approximately periodic time series is found, synchronisation-like phenomena could be used to determine if a novel time series is similar to the members of the learning set. The most crucial step of the classification procedure outlined before is the automatic generation of a chaotic model from data, called identification. Originally, this was done using a simple low dimensional reference model. Here, on the other hand, a biologically inspired approach is taken. This has the advantage that the identification and classification procedure could be greatly simplified and the computational power involved significantly reduced. The biologically inspired model used for identification was announced several time in literature under the name "Echo State Network". The articles available on it consisted mainly of examples were it performed remarkably well, though a thorough analysis was still missing to the scientific community. Here the model is analysed using a measure that had appeared already in similar contexts and with help of this measure good settings of the models' parameter were determined. Finally, the model was used to assess if stochastic patterns can be modelled by chaotic signals. Indeed, it has been shown that, for the biologically inspired modelling technique considered, chaotic behaviour appears to implicitly model diversity and randomness of the learnt patterns whenever these are sufficiently structured; whilst chaos does not appear when the patterns are remarkably unstructured. In other words, deterministic chaos or strongly coloured noise lead to the chaos emergence as opposed to white-like noise which does not. With this result in mind, the classification of gait signals was attempted, as no signs of chaoticity could be found in them and the previously available modelling technique seemed to have difficulties to model their diversity. The identification and classification results with the biologically inspired model turned out to be very good