An investigation into the techniques for detecting hedgehogs in a rural landscape

Various techniques and devices have been developed for the purpose of detecting wildlife but many only provide optimum results in particular habitats, for certain species or under ideal weather conditions. It is therefore advantageous to understand the efficiency and suitability of techniques under different scenarios. The effectiveness of methods for detecting rural Irish hedgehogs was investigated as part of a larger study in April 2008. Road kill sightings and questionnaires were employed to locate possible hedgehog sites. Six sites were subsequently selected, and in these areas trapping, spotlighting and foot print tunnels were employed to investigate whether hedgehogs were indeed in the surrounding landscape. Infrared thermal imagery was examined as a detection device. Trapping and infrared imagery failed to detect hedgehogs in areas where they had previously been recorded. Footprint tunnels proved to be unsuccessful in providing absolute proof of hedgehogs in an area. No single method of detection technique could be relied upon to conclude the presence of hedgehogs in an area. A combination of methods is therefore recommended. However, spotlighting was the most effective method, taking a mean of 4 nights to detect a hedgehog, in comparison to 48 nights if footprint tunnels were used as a sole method of detection. This was also suggested by rarefaction curves of these two detection techniques, where over a 48 night period hedgehogs were expected to be recorded 27 times through spotlighting and just 5 times in an equivalent period of footprint tunnel nights.

Modelling and predictive control techniques for building heating systems

Model predictive control (MPC) has often been referred to in literature as a potential method for more efficient control of building heating systems. Though a significant performance improvement can be achieved with an MPC strategy, the complexity introduced to the commissioning of the system is often prohibitive. Models are required which can capture the thermodynamic properties of the building with sufficient accuracy for meaningful predictions to be made. Furthermore, a large number of tuning weights may need to be determined to achieve a desired performance. For MPC to become a practicable alternative, these issues must be addressed. Acknowledging the impact of the external environment as well as the interaction of occupants on the thermal behaviour of the building, in this work, techniques have been developed for deriving building models from data in which large, unmeasured disturbances are present. A spatio-temporal filtering process was introduced to determine estimates of the disturbances from measured data, which were then incorporated with metaheuristic search techniques to derive high-order simulation models, capable of replicating the thermal dynamics of a building. While a high-order simulation model allowed for control strategies to be analysed and compared, low-order models were required for use within the MPC strategy itself. The disturbance estimation techniques were adapted for use with system-identification methods to derive such models. MPC formulations were then derived to enable a more straightforward commissioning process and implemented in a validated simulation platform. A prioritised-objective strategy was developed which allowed for the tuning parameters typically associated with an MPC cost function to be omitted from the formulation by separation of the conflicting requirements of comfort satisfaction and energy reduction within a lexicographic framework. The improved ability of the formulation to be set-up and reconfigured in faulted conditions was shown.