Physical Sciences and Mathematics Commons^™

Automatic Fracture Orientation Extraction From Sfm Point Clouds, Jon Kissi

Electronic Thesis and Dissertation Repository

Geology seeks to understand the history of the Earth and its surface processes through charac- terisation of surface formations and rock units. Chief among the geologists’ tools are rock unit orientation measurements, such as Strike, Dip and Dip Direction. These allow an understanding of both surface and sub-structure on both the local and macro scale.

Although the way these techniques can be used to characterise geology are well understood, the need to collect these measurements by hand adds time and expense to the work of the geologist, precludes spontaneity in field work, and coverage is limited to where the geologist …

Collective Contextual Anomaly Detection For Building Energy Consumption, Daniel Berhane Araya

Electronic Thesis and Dissertation Repository

Commercial and residential buildings are responsible for a substantial portion of total global energy consumption and as a result make a significant contribution to global carbon emissions. Hence, energy-saving goals that target buildings can have a major impact in reducing environmental damage. During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes the \textit{ensemble anomaly detection} (EAD) framework. The EAD is …

Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is …