Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

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.

Supporting therapist engagement in evidence based practice: Developing a continuing education programme through participatory action research

Therapists find it challenging to integrate research evidence into their clinical decision-making because it may involve modifying their existing practices. Although continuing education (CE) programmes for evidence-based practice (EBP) have employed various approaches to increase individual practitioner’s knowledge and skills, these have been shown to have little impact in changing customary behaviours. To date, there has been little attempt to actively engage therapists as collaborators in developing educational processes concerning EBP. The researcher collaborated with seven clinical therapists (one occupational therapist, four physiotherapists and two speech and language therapists) enrolled in a new post-qualification Implementing Evidence in Therapy Practice (IETP) MSc module to monitor and adapt the learning programme over ten weeks. The participating therapists actively engaged in participatory action research (PAR) iterative cycles of reflecting→ planning→ acting→ observing→ reflecting with the researcher. Mixed methods were used to evaluate the IETP module and its influence on therapists’ subsequent engagement in EBP activities. Data were gathered immediately on completion of the module and five months later. Immediate post-module findings revealed four components as being important to the therapists: 1) characteristics of the learning environment; 2) acquisition of relevant EBP skills; 3) nature of the learning process; and 4) acquiring confidence. The two themes and sub-themes which emerged from individual interviews conducted five months post-module expanded on the four components already identified. Theme 1: Experiencing the learning (sub-themes: module organisation; learning is relational; improving the module); and theme 2: Enacting the learning through a new way of being (sub-themes: criticality and reflection; self agency; modelling EBP behaviours; positioning self in an EB work culture). The therapists’ perspectives had by then shifted from that of a learner to that of a clinician constructing a new sense of self as an evidence-based practitioner. Findings from this study underline the importance of the process of socially constructed knowledge and of empowering learners through collaboratively designed continuing education programmes. In the student-driven learning environment, therapists chose repetitive skill-building and authentic problem-solving activities which reflected the complexity of the environments to which they were expected to transfer their learning. These findings have implications for educators designing EBP continuing education programmes, during which students develop professional ways of being.