Low-rate non-intrusive load monitoring approaches via graph signal processing

The large-scale roll-out of smart metering worldwide brings many new application possibilities. One promising application is appliance-level energy feedback based on identifying individual loads from aggregate measurements. Driven by high application potentials, the research in this area has intensified. In particular, non-intrusive load monitoring (NILM), that is, estimating appliance load consumption from aggregate readings, using software means only, has attracted a lot of attention, since it does not require any additional hardware to be installed. This thesis first proposes two Graph Signal Processing (GSP)-based approaches for disaggregation of total energy consumption down to individual appliances used. The first approach uses the Graph Laplacian Regularisation (GLR) minimiser results as a starting point, adding further refinement via Simulated Annealing (SA). The second approach applies data segmentation and associates data segments with graph nodes. A Dynamic Time Warping (DTW) distance is applied for evaluating weights between graph nodes. GLR minimiser is again used for clustering. Finally, a generic optimisation based approach is proposed for improving the accuracy of existing NILM by minimising the difference between the measured aggregate load and the sum of estimated individual loads with the difference from original NILM approaches' results as regularisation. For all proposed methods, the competitive performance are demonstrated in terms of both accuracy and effciency compared to state-of-the-art approaches, using the public Personalised Retrofit Decision Support Tools For UK Homes Using Smart Home Technology (REFIT) dataset and Reference Energy Disaggregation Dataset (REDD) electrical load datasets.The large-scale roll-out of smart metering worldwide brings many new application possibilities. One promising application is appliance-level energy feedback based on identifying individual loads from aggregate measurements. Driven by high application potentials, the research in this area has intensified. In particular, non-intrusive load monitoring (NILM), that is, estimating appliance load consumption from aggregate readings, using software means only, has attracted a lot of attention, since it does not require any additional hardware to be installed. This thesis first proposes two Graph Signal Processing (GSP)-based approaches for disaggregation of total energy consumption down to individual appliances used. The first approach uses the Graph Laplacian Regularisation (GLR) minimiser results as a starting point, adding further refinement via Simulated Annealing (SA). The second approach applies data segmentation and associates data segments with graph nodes. A Dynamic Time Warping (DTW) distance is applied for evaluating weights between graph nodes. GLR minimiser is again used for clustering. Finally, a generic optimisation based approach is proposed for improving the accuracy of existing NILM by minimising the difference between the measured aggregate load and the sum of estimated individual loads with the difference from original NILM approaches' results as regularisation. For all proposed methods, the competitive performance are demonstrated in terms of both accuracy and effciency compared to state-of-the-art approaches, using the public Personalised Retrofit Decision Support Tools For UK Homes Using Smart Home Technology (REFIT) dataset and Reference Energy Disaggregation Dataset (REDD) electrical load datasets

Tier I Rti For English Language Learners With Language Deficits

Educators are attempting to eliminate the disproportionate representation of culturally and linguistically diverse students in special education through initiatives such as Response to Intervention (RtI). Prior to the implementation of such initiatives, existing research relevant to this population must be critically reviewed and expanded. A synthesis of the available literature can provide significant insight into the type of data necessary to make informed decisions involving English language learners (ELL) at Tier I of an RtI model. In forming the theoretical foundation for this research, cognitive deficits associated with language-based disabilities and principles of cognitive load theory were examined. The study is an investigation of the following research question: Is the effectiveness of the bilingual English as a Second Language (ESL) model significantly altered under certain conditions? The research question was addressed through testing moderator effects using hierarchical linear regression. Initial English proficiency and initial Spanish proficiency were examined as moderating variables of the relationship between ESL model type and Kindergarten academic achievement. Academic achievement was defined as student learning growth on the Florida Assessment for Reading Instruction (FAIR) and student outcome scores on the Comprehensive English Language Learning Assessment (CELLA) Listening/Speaking and Reading constructs. Results supported: a) the relationship between initial English proficiency and FAIR growth, CELLA Listening/Speaking, and CELLA Reading, b) the relationship between initial Spanish proficiency and FAIR growth and CELLA Listening/Speaking, c) the relationship between type of ESL model and FAIR growth, CELLA Listening/Speaking, and CELLA iii Reading, d) the additional effect of the interaction of initial Spanish language proficiency with ESL model type to alter FAIR learning growth over time, and e) the additional effect of the interaction of initial English language proficiency with ESL model type to alter CELLA Listening/Speaking scores. Overall, this research supports the hypothesis that initial language proficiency can significantly alter the effectiveness of a bilingual ESL model. Recommendations for future research in this area include longitudinal studies using a similar hierarchical regression design with moderators in order to contextualize positive student outcomes

A longitudinal analysis of motivation profiles at work

This paper examines the multidimensional nature of workplace motivation and the importance of a continuum structure in self-determination theory through application of complementary variable- and person-centered approaches. This approach is taken to simultaneously model the complexity of motivation and highlight interactions between motivational factors. Additionally, this study represents an initial test of the temporal stability of work motivation profiles. A sample of 510 full-time employees were recruited from a range of occupations. Results support the central importance of a general factor representing self-determination as the most influential factor in an employee’s motivation profile. However, smaller effects associated with the motivation subscales, especially identified regulation, were also noticed. Importantly, motivation profiles were found to be highly stable over the 4-month duration of this study. Results lend support to the theoretical position that while general self-determination is an essential component of motivation, it alone does not fully describe an employee’s motivation